500.112 (E) |
GATEWAY COMPUTING: JAVA (3) staff This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. |
MWF 50 minutes, limit 19/section |
601.104 (H) |
COMPUTER ETHICS (1) Leschke Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law -- specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. |
Sections meet during the first 8 weeks of the semester
only. Sec 01: Mon 7:00-8:30p |
601.124 (EH) |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez The expansion of artificial intelligence
(AI)-enabled use cases across a broad spectrum of domains has
underscored the benefits and risks of AI. This course will address the
various ethical considerations engineers need to engage with to build
responsible and trustworthy AI-enabled autonomous systems. Topics to
be covered include: values-based decision making, ethically aligned
design, cultural diversity, safety, bias, AI explainability, privacy,
AI regulation, the ethics of synthetic life, and the future of
work. Case studies will be utilized to illustrate real-world
applications. Students will apply learned material to a group research
project on a topic of their choice. |
Sec 01: MW 1:30-2:45p |
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. The course includes usage of Linux tools for file manipulation, editing and programming, and git for versioning. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: 500.132/133/134 OR (C+/S*/S** or better grade in 500.112/113/114) or AP CS or equivalent. |
Sec 01 (Darvish): MWF 10:00-11:15a, limit 35 |
601.226 (EQ) |
DATA STRUCTURES (4) Simari This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: 500.132 OR (C+/S*/S** or better grade in 500.112 or 601.220) or AP CS credit or equivalent. |
Sec 01: MWF 12-1:15p |
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments. Prereq: 601.220. |
MWF 10a, limit 130 |
601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) More This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Pre/co-req: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. |
Sec 01: MWF 9-9:50a, W 4:30-5:20p |
601.277 (QS) |
DISINFORMATION SELF-DEFENSE (3) Shpitser Scientific, statistical and logical literacy is a necessary skill for evaluating policy proposals, reading news articles with an appropriately critical eye, and making informed choices as consumers and voters. Misunderstanding of claims made in scientific publications, online publishing platforms, and mass media drives, in part, the spread of malicious misinformation and propaganda online. Further, many actors have the means, the motive and the opportunity to mislead the public in a variety of subtle and not so subtle ways. This class will give you tools to discern valid and invalid forms of inference and discourse, and give you tools to communicate precisely, argue appropriately, and stay on top of research and news with an appropriately skeptical attitude. A use case used throughout the class will be online disinformation surrounding the COVID-19 pandemic. The class will draw on historical and modern literature on linguistic, logical, and probabilistic fallacies, statistical and logical inference, data visualization, cognitive biases, and the scientific method. Prereq: EN.601.230 MFCS, EN.553.171/EN.553.172 Discrete Math or AS.150.118 Intro to Formal Logic, or equivalent, or permission. |
TuTh 3-4:15p |
601.290 (E) |
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. [Oral] Prereq: 601.220 and 601.226. |
MWF 3-4:15p Sec 02: limit 30, CS sophomore majors only until 12/3 |
601.350 (E) |
GENOMIC DATA SCIENCE (3) Salzberg [Formerly Intro to Genomic Research] This course will use a project-based approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific literature by reading high-profile research papers that generated groundbreaking or controversial results. [Applications] Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.
|
TuTh 3-4:15 |
601.404 (E) |
BRAIN & COMPUTATION (1) Kosaraju Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Prerequisites: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. |
Tu 4:30-5:20p |
601.411 (E) |
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a two-course sequence and is a continuation of course 660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. Prerequisite: 660.410. |
MW 12-1:15p |
601.413 (E) |
SOFTWARE DEFINED NETWORKS (3) Sabnani
Software-Defined Networks (SDN) enable programmability of data
networks and hence rapid introduction of new services. They use
software-based controllers to communicate with underlying
hardware infrastructure and direct traffic on a network. This
model differs from that of traditional networks, which use
dedicated hardware devices (i.e., routers and switches) to
control network traffic. Prerequisite: EN.601.414/614. Students can receive credit for EN.601.413 or EN.601.613, but not both. |
Tu 4:30-7p |
601.414 (E) |
COMPUTER NETWORKS (3) Marder Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. [Systems] Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
MW 3-4:15p |
601.418 (E) |
OPERATING SYSTEMS (3) Hovemeyer
This course covers the fundamental topics related to operating systems theory
and practice. Topics include processor management, storage management,
concurrency control, multi-programming and processing, device drivers,
operating system components (e.g., file system, kernel), modeling and
performance measurement, protection and security, and recent innovations in
operating system structure. Course work includes the implementation of
operating systems techniques and routines, and critical parts of a small but
functional operating system. [Systems]
Prereq: 601.226 & 601.229. Students may
receive credit for only one of 601.318/418/618. |
MW 12-1:15p |
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Madooei This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [(Systems or Applications), Oral] Prereq: 601.226 & 601.220 & (EN.601.280 or EN.601.290 or permission). Students may receive credit for only one of 601.421/621. |
MWF 4:30-5:20p |
601.422 (E) |
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems] Pre-req: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both. |
MWF 1:30-2:20p |
601.425 (E) |
SOFTWARE SYSTEM DESIGN (3) Madooei This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Prereq: EN.601.315/415/615 or EN.601.280 or EN.601.290 or EN.601.340/440/640 or EN.601.421/621), or permission. Students may receive credit for only one of 601.425/625. |
TuTh 12-1:15p |
601.426 (EQ) |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Required course background: 601.226. Freshmen and sophomores by permission only. |
MW 1:30-2:45 |
601.430 (EQ) |
COMBINATORICS AND GRAPH THEORY IN CS Li This course covers the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Pre-requisite: 601.230 OR 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630. |
TuTh 1:30-2:45pm |
601.433 (EQ) |
INTRO ALGORITHMS (3) Garg
This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. [Analysis] Prereq: 601.226 and (553.171/172 or 601.231 or 601.230) or Perm. Req'd. Students may receive credit for only one of 601.433/633. |
Sec 01: MW 1:30-2:45p |
601.435 (EQ) |
APPROXIMATION ALGORITHMS (3) Dinitz This course provides an introduction to approximation algorithms. Topics include vertex cover, TSP, Steiner trees, cuts, greedy approach, linear and semi-definite programming, primal-dual method, and randomization. Additional topics will be covered as time permits. There will be a final project. [Analysis] Prereq: 601.433/633 or permission. Students may receive credit for only one of 601.435/635. |
TuTh 9-10:15 |
601.445 (E)
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. [Systems] Prereq: 601.226 and 601.229. Students may receive credit for only one of 601.445/645. |
MW 3-4:15 |
601.446 (E) |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. [Analysis] Pre-req: 600/601.226. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9:00-10:15 |
580.448 (E) CSCI-APPL
|
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle [Cross-listed from BME.] The demand for computational genomics and biology in industry, academia, and government is rapidly growing. Genomic data have the potential to reveal causes of disease, novel drug targets, and relationships among genes and pathways in our cells. However, identifying meaningful patterns from high-dimensional genomic data has required development of new computational tools. This course gives a broad introduction to statistics, machine learning, and computational tools for modern computational genomics real-world problems. Assignments include project sets including programming, and a project component. Genomic applications will include single-cell RNA-sequencing, genome-wide association studies, epigenetics, spatial transcriptomics, heritability and genetic risk prediction, long-read RNA-sequencing, and ethics in genetics. Prereq: EN.580.475 or permission. |
MW 3-4:15 |
601.453 (E) |
APPLICATIONS OF AUGMENTED REALITY (3) Martin-Gomez
This course is designed to expand the student’s augmented reality
knowledge and introduce relevant topics necessary for developing more
meaningful applications and conducting research in this field. The
course addresses the fundamental concepts of visual perception and
introduces non-visual augmented reality modalities, including
auditory, tactile, gustatory, and olfactory applications. The
following sessions discuss the importance of integrating user-centered
design concepts to design meaningful augmented reality applications. A
later module introduces the basic requirements to design and conduct
user studies and guidelines on interpreting and evaluating the results
from the studies. During the course, students conceptualize, design,
implement and evaluate the performance of augmented reality solutions
for their use in industrial applications, teaching and training, or
healthcare settings. Homework in this course will relate to applying
the theoretical methods used for designing, implementing, and
evaluating augmented reality applications. Students may receive credit
for only one of 601.453/653. Prerequisites: EN.601.454/654. |
TuTh 1:30-2:45p |
601.456 (E) |
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who wish to use this course to satisfy the "Team" requirement should register for EN.601.496 instead. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for 601.356. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of
601.456, 601.496, 601.656. |
TuTh 1:30-2:45 |
601.461 (EQ) |
COMPUTER VISION (3) Katyal This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo; and object recognition. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
Tue 4:30-7p Sec 02: limit 5, CIS/Robotics minors only until 12/3 |
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED ROBOTICS (3) Leonard This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. [Analysis] Prereq: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats in-depth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 3:00-4:15p |
601.466 (E) |
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Required course background: 601.226. |
TuTh 3-4:15 |
601.471 (E) |
NLP: SELF-SUPERVISED MODELS (3) Khashabi The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Pre-reqs: EN.601.226, one of (EN.601.465/665, EN.601.468/668, EN.601.475/675, EN.601.482/682), Linear Algebra, and Probability, as well as familiarity with Python/PyTorch. |
TuTh 9-10:15a |
601.472 (E) |
NATURAL LANGUAGE PROCESSING FOR COMPUTATIONAL SOCIAL SCIENCE (3) Field
[Alt. title: Analyzing Text as Data] Vastly available digitized text
data has created new opportunities for understanding social
phenomena. Relatedly, social issues like toxicity, discrimination,
and propaganda frequently manifest in text, making text analyses
critical for understanding and mitigating them. In this course, we
will centrally explore: how can we use NLP as a tool for
understanding society? Students will learn core and recent advances
in text-analysis methodology, building from word-level metrics to
embeddings and language models as well as incorporating statistical
methods such as time series analyses and causal
inference. [Applications] Pre-reqs: one of (EN.601.465/665, EN.601.467/667, EN.601.468/668) and familiarity with Python/PyTorch. Students may receive credit for EN.601.472 or EN.601.672, but not both. |
MW 1:30-2:45p |
601.473 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. [Reasoning]
Pre-reqs: Calc III & Prob/Stat & Linear Algebra & Computing
[AS.110.202 AND ((EN.553.420 OR EN.553.421) AND (EN.553.430 OR
EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND
(AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112
OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205)]. |
TuTh 1:30-2:45p |
601.475
(E) |
MACHINE LEARNING (3) Liu
The goal of machine learning (a subfield of artificial intelligence)
is the development of computational systems, methods, and algorithms
that can learn from data to improve their performance. This course
introduces the foundational concepts of modern Machine Learning,
including core principles, popular algorithms and modeling platforms.
This will include both supervised learning, which includes popular
algorithms like SVMs, logistic regression and deep learning, as well
as unsupervised learning frameworks, which include Expectation
Maximization and graphical models. Homework assignments include both a
heavy programming components as well as analytical questions that
explore various machine learning concepts. This class will build on
prerequisites that include probability, linear algebra, multivariate
calculus and basic optimization.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
MWF 3-4:15p |
EN.601.484 (E) |
ML: INTERPRETABLE MACHINE LEARNING DESIGN (3) Unberath There are considerable research thrusts that seek to increase the trustworthiness and perceived reliability of machine learning solutions. One such thrust, interpretable machine learning, attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, interpretability is not a property of machine learning algorithms, but an affordance: a relationship between interpretable model and the target users in their context. Successful development of machine learning solutions that afford interpretation thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, during student team-delivered lectures, we will learn about contemporary techniques to introduce interpretability to machine learning models and discuss recent literature on the topic. In addition to hands-on homework assignments, students will work in groups to design, justify, implement, and test an interpretable machine learning algorithm for a problem of their choosing. Pre-reqs: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) Reiter
This course is designed to introduce undergraduate and graduate
students to design techniques and practices in human-computer
interaction (HCI), the study of interactions between humans and
computing systems. Students will learn design techniques and
evaluation methods, as well as current practices and exploratory
approaches, in HCI through lectures, readings, and
assignments. Students will practice various design techniques and
evaluation methods through hands-on projects focusing on different
computing technologies and application domains. This course is
intended for undergraduate and graduate students in Computer
Science/Cognitive Science/Psychology. Interested students from
different disciplines should contact the instructor before enrolling
in this course. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Mon 4:30-7p |
601.496 (E) |
COMPUTER INTEGRATED SURGERY II - TEAMS (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project in teams of at least 3 students, under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who prefer to do individual projects must register for EN.601.456 instead. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for
only one of 601.456, 601.496, 601.656. |
TuTh 1:30-2:45 |
601.501 |
COMPUTER SCIENCE WORKSHOP An independent applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
601.503 |
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission of faculty sponsor is required.
|
See below for faculty section numbers. |
601.507 |
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers and whether to select 507 or 517. |
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit, which is the limit per semester. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
601.513 |
GROUP UNDERGRADUATE PROJECT Independent learning and application for undergraduates under the direction of a faculty member in the department. This course has a regular project group meeting that students are expected to attend. The individual project contributions, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
601.517 |
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
601.520 |
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 601.519. Prerequisite: 601.519 |
See below for faculty section numbers. |
601.604 (E) |
BRAIN & COMPUTATION (1) Kosaraju Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Required course background: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. |
Tu 4:30-5:20p |
601.611 |
CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410. |
MW 12-1:15p |
601.613 (E) |
SOFTWARE DEFINED NETWORKS (3) Sabnani Software-Defined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use software-based controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic. This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications. A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments. Required Course Background: computer networks. Students can receive credit for EN.601.413 or EN.601.613, but not both. |
Tu 4:30-7p |
601.614 |
COMPUTER NETWORKS Marder Same as 601.414, for graduate students. [Systems] Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
MW 3-4:15p |
601.618 |
OPERATING SYSTEMS Hovemeyer
Same material as 601.418, for graduate students. [Systems]
Required course background: Data Structures & Computer System
Fundamentals. Students may
receive credit for only one of 601.318/418/618. |
MW 12-1:15p |
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Madooei This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. |
MWF 4:30-5:20p |
601.622 |
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems] Required course background: significant mobile or web app development. Students may receive credit for 601.422 or 601.622, but not both. |
MWF 1:30-2:20p |
601.625 |
SOFTWARE SYSTEM DESIGN (3) Madooei This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Required course background: (EN.601.315/415/615 or EN.601.280 or EN.601.290 or EN.601.340/440/640 or EN.601.421/621), or permission. Students may receive credit for only one of 601.425/625. |
TuTh 12-1:15p |
601.626 |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Same as 601.426, for graduate stuents. Students may receive credit for only one of 601.426/626. [Analysis] Required course background: 601.226. |
MW 1:30-2:45 |
601.630 |
COMBINATORICS AND GRAPH THEORY IN CS Li This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Required Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630. |
TuTh 1:30-2:45p |
601.633 |
INTRO ALGORITHMS Garg
Same as 601.433, for graduate students. [Analysis] Required Background: data structures, discrete math, proof writing. Students may receive credit for only one of 601.433/633. |
MW 1:30-2:45p |
601.635 |
APPROXIMATION ALGORITHMS Dinitz Same as 601.435, for graduate students. [Analysis] Required course background: 601.433/633 or permission. Students may receive credit for only one of 601.435/635. |
TuTh 9-10:15 |
601.645
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS Green Same material as 601.445, for graduate students. [Systems] Required Course Background: knowledge of data structures and computer system fundamentals. Students may receive credit for only one of 601.445/645. |
MW 3-4:15 |
601.646 |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. [Analysis] Pre-req: Data Structures. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9-10:15 |
601.653 (E) |
APPLICATIONS OF AUGMENTED REALITY (3) Martin-Gomez
This course is designed to expand the student’s augmented reality
knowledge and introduce relevant topics necessary for developing more
meaningful applications and conducting research in this field. The
course addresses the fundamental concepts of visual perception and
introduces non-visual augmented reality modalities, including
auditory, tactile, gustatory, and olfactory applications. The
following sessions discuss the importance of integrating user-centered
design concepts to design meaningful augmented reality applications. A
later module introduces the basic requirements to design and conduct
user studies and guidelines on interpreting and evaluating the results
from the studies. During the course, students conceptualize, design,
implement and evaluate the performance of augmented reality solutions
for their use in industrial applications, teaching and training, or
healthcare settings. Homework in this course will relate to applying
the theoretical methods used for designing, implementing, and
evaluating augmented reality applications. Students may receive credit
for only one of 601.453/653. Required course background: intermediate programming (C/C++), data structures, linear algebra; EN.601.654 preferred. |
TuTh 1:30-2:45p |
601.656 |
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. [Applications] Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/496/656. |
TuTh 1:30-2:45 |
601.661 |
COMPUTER VISION Katyal Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
Tue 4:30-7p Sec 02: limit 15, Robotics + Data Science grads [Sec 03: limit 10, closed for now] |
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same as 601.463, for graduate students. [Analysis] Required course background: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
601.664 |
ARTIFICIAL INTELLIGENCE Koehn Same as 601.464, for graduate students. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 1:30-2:45p |
601.666 |
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666. Required course background: 601.226. |
TuTh 3-4:15p |
601.671 (E) |
NLP: SELF-SUPERVISED MODELS (3) Khashabi The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch, natural language processing or machine learning. Pre-reqs: one of EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675. |
TuTh 9-10:15a |
601.672 |
NATURAL LANGUAGE PROCESSING FOR COMPUTATIONAL SOCIAL SCIENCE (3) Field
[Alt. title: Analyzing Text as Data]
Vastly available digitized text data has created new opportunities for
understanding social phenomena. Relatedly, social issues like
toxicity, discrimination, and propaganda frequently manifest in
text, making text analyses critical for understanding and mitigating
them. In this course, we will centrally explore: how can we use NLP
as a tool for understanding society? Students will learn core and
recent advances in text-analysis methodology, building from
word-level metrics to embeddings and language models as well as
incorporating statistical methods such as time series analyses and
causal inference. [Applications] Required Course Background: natural language processing and familiarity with Python/PyTorch. Students may receive credit for EN.601.472 or EN.601.672, but not both. |
MW 1:30-2:45p |
601.673 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. [Reasoning]
Required Course Background: Calc III & Prob/Stat & Linear Algebra &
Computing; prior course in ML/AI strongly recommended.
|
TuTh 1:30-2:45p |
601.675 |
MACHINE LEARNING Liu
Same as 601.475, for graduate students.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. |
MWF 3-4:15p |
EN.601.684 (E) |
ML: INTERPRETABLE MACHINE LEARNING DESIGN (3) Unberath There are considerable research thrusts that seek to increase the trustworthiness and perceived reliability of machine learning solutions. One such thrust, interpretable machine learning, attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, interpretability is not a property of machine learning algorithms, but an affordance: a relationship between interpretable model and the target users in their context. Successful development of machine learning solutions that afford interpretation thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, during student team-delivered lectures, we will learn about contemporary techniques to introduce interpretability to machine learning models and discuss recent literature on the topic. In addition to hands-on homework assignments, students will work in groups to design, justify, implement, and test an interpretable machine learning algorithm for a problem of their choosing. Required course background: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION Reiter
Same material as EN.601.490, for graduate students. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Mon 4:30-7p |
601.716 NEW COURSE! |
ADVANCED TOPICS IN INTERNET OF THINGS Zhao This course explores the convergence of computer networks, mobile computing, and embedded systems, with a specific focus on the Internet of Things (IoT). IoT represents a paradigm shift in computing, aiming to bridge the gap between the physical and digital worlds. Its development has opened up new possibilities, including mobile health, smart homes, industrial automation, and more. Throughout the course, students will delve into advanced topics such as IoT networking, mobile and edge computing, embedded machine learning, wireless sensing, human-computer interaction, and mobile health applications. To excel in this course, students are expected to engage in pre-class readings and in-class discussions, and complete a semester-long project. The focus of the course will be on training research philosophy and principles instead of papers' technical details. The course covers multiple disciplines and encourages interdisciplinary projects; students with diverse backgrounds such as computer science/engineering, electrical engineering, biomedical engineering or other related areas are welcomed. [Systems] Recommended Course Background: familiarity with computer system fundamentals, computer networks, signal processing, and mobile computing. |
TuTh 12-1:15p |
601.764 |
ADVANCED NLP: MULTILINGUAL METHODS Murray This is a project based course focusing on the design and implementation of systems that scale Natural Language Processing methods beyond English. The course will cover both multilingual and cross-lingual methods with an emphasis on zero-shot and few-shot approaches, as well as ‘silver’ dataset creation. Modules will include Cross-Lingual Information Extraction & Semantics, Cross-Language Information Retrieval, Multilingual Question Answering, Multilingual Structured Prediction, Multilingual Automatic Speech Recognition, as well as other non-English centric NLP methods. Students will be expected to work in small groups and pick from one of the modules to create a model based on state-of-the-art methods covered in the class. The course will be roughly two-thirds lecture based and one-third students presenting project updates periodically throughout the semester. [Applications] Prerequisite: 601.465/665 NLP; Machine Translation recommended. |
TuTh 1:30-2:45 |
601.779 |
MACHINE LEARNING: ADVANCED TOPICS Arora This course will focus on recent advances in machine learning. Topics will vary from year to year. The course will be project focused and involve presenting and discussing recent research papers. Required Course Background: prior experience in machine learning. Instructor permission required. |
Fri 12-2:30p |
601.783 |
VISION AS BAYESIAN INFERENCE (3) Yuille This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis or Applications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.550.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475/675) suggested but not required. |
TuTh 9-10:15a |
601.792 |
ADVANCED TOPICS IN CONVERSATIONAL USER INTERFACES Xiao As a critical component of human-computer interaction, conversational user interfaces (CUIs) have the potential to revolutionize the way we interact with technology. This course is designed for graduate students who want to gain a deeper understanding of CUIs and their real world applications. Throughout the course, students will explore cutting-edge research and methodologies for designing, implementing, and evaluating CUIs. Various forms of conversational interface will be covered, including chatbots, voice assistants, and multimodal dialogue systems. Coursework will include short open-ended assignments focused on applying methods learned in class, reading recent papers, and a course project. [Software] Prerequisite: 601.490/690 or permission. |
MW 3-4:15p |
601.801 |
Required for all CS PhD students. Strongly recommended for MSE students. Only 1st & 2nd year PhD students should formally register. |
TuTh 10:30-11:45 |
601.803 |
MASTERS RESEARCH Independent research for masters or pre-dissertation PhD students. Permission required. |
See below for faculty section numbers. |
601.805 |
GRADUATE INDEPENDENT STUDY Permission Required. |
See below for faculty section numbers. |
601.807 |
TEACHING PRACTICUM Selinski/Smith PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required. |
limit 25, P/F only |
601.809 |
PHD RESEARCH |
See below for faculty section numbers. |
601.810 |
DIVERSITY & INCLUSION IN COMPUTER SCIENCE & ENGINEERING Kazhdan This reading seminar will focus on the question of diversity and inclusion in computer science (in particular) and engineering (in general). We aim to study the ways in which the curriculum, environment, and structure of computer science within academia perpetuates biases alienating female and minoritized students, and to explore possible approaches for diversifying our field. The seminar will meet on a weekly basis, readings will be assigned, and students will be expected to participate in the discussion. |
Wed 3p |
601.826 |
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This seminar course covers recent developments in the foundations of programming language design and implementation. topics covered include type theory, process algebra, higher-order program analysis, and constraint systems. Students will be expected to present papers orally. |
Fri 11-12 |
601.831 |
CS THEORY SEMINAR Dinitz, Li Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers. |
W 12 |
601.856 |
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Co-listed with 520.746. |
Tu 3-4:15 |
601.857 | SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. |
Tu 3-4:15 |
601.864 |
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on Cross-Language Information Retrieval, Cross-Lingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other non-English NLP sub-fields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465/665. |
M 1:30 |
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner
A reading group exploring important current research in the field
and potentially relevant material from related fields. In addition to
reading and discussing each week's paper, enrolled
students are expected to take turns selecting papers and leading the discussion. |
Wed 12-1:15 |
601.866 |
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. |
Fr 10-10:50 |
601.867 NEW COURSE! |
SELECTED TOPICS IN TRUSTWORTHY & RESPONSIBLE NLP Chen & Levy This is a graduate student seminar aimed at introducing graduate students to the research areas of trustworthy and responsible NLP. This is primarily targeted at students with an NLP background and no or very little background in the course topics: bias, privacy, safety, misinformation, explainability, interpretability, robustness. Students will be expected to read, present and discuss papers each week. Required Background: NLP experience (601.665, 601.667, 601.668, 601.671, 601.765, etc.). |
Th 3-4:15p |
601.868 |
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. |
M 11-12 |
620.745 |
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, Whitcomb, Vidal, Etienne-Cummings Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided intervention. Sensor based robotics, including computer vision and biomedical image analysis. Algorithmic robotics, robot control and machine learning. Autonomous robotics for monitoring, exploration and manipulation with applications in home, environmental (land, sea, space), and defense areas. Biorobotics and neuromechanics, including devices, algorithms and approaches to robotics inspired by principles in biomechanics and neuroscience. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. |
Wed 12-1:30 |
520.702 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Trmal CLSP seminar series, for any students interested in current topics in language and speech processing. |
Mon & Fri 12-1:15 |
580.458/658 |
COMPUTING THE TRANSCRIPTOME (3) Pertea The primary goal of this course is for students to learn the leading computational tools used in the field of transcriptomics, as well as the theory concepts behind them, in order to be able to analyze the genes and transcripts expressed in a living cell. Lectures will cover different practical ways to analyze large data sets generated by high-throughput RNA sequencing (RNA-Seq) experiments, including alignment, assembly, and quantification. You will learn about different technologies of RNA-seq and how they influence the transcriptome you are computing, what are the best practices for RNA-seq data analysis, what are the methods for transcriptome assembly and quantification, how do you measure the transcript expression levels, how do you find which genes are differentially expressed between different RNA-seq datasets, and how do you visualize your results. Prereq: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python. Familiarity with R recommended. |
TuTh 4:30-5:45p |
650.624 |
NETWORK SECURITY Johnston
This course focuses on communication security in computer systems and
networks. The course is intended to provide students with an
introduction to the field of network security. The course covers
network security services such as authentication and access control,
integrity and confidentiality of data, firewalls and related
technologies, Web security and privacy. Course work involves
implementing various security techniques. A course project is
required. |
TuTh 3-4:15p |
650.631 |
ETHICAL HACKING Watkins Cyber security affects every facet of industry and our government, and thus is now a threat to National Security. This course is designed to introduce students to the skills needed to defend computer network infrastructure by exposing them to the hands-on identification and exploitation of vulnerabilities in servers (i.e., Windows and Linux), wireless networks, websites, and cryptologic systems. These skills will be tested by having teams of students develop and participate in instructor lead capture-the-flag competitions. Also included are advanced topics such as shell coding, IDA Pro analysis, fuzzing, and writing or exploiting network-based applications or techniques such as web servers, spoofing, and denial of service. |
Th 4:30-7p |
650.640 |
MORAL & LEGAL FOUNDATIONS OF PRIVACY Matthew Welling This course explores the ethical and legal underpinnings of the concept of privacy. It examines the nature and scope of the right to privacy by addressing fundamental questions such as: What is privacy? Why is privacy morally important? How is the right to privacy been articulated in constitutional law? |
Tu 4:30-6:45p |
650.654 |
COMPUTER INTRUSION DETECTION Li Intrusion detection supports the on-line monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in host-centered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue in-depth study of special topics of interest in course projects. |
MW 12-1:15p |
650.667 |
MOBILE DEVICE FORENSICS Leschke This course introduces the student to the field of applied Mobile Device Forensics as practiced by corporate security and law enforcement personnel. The emphasis is on "live" (powered-on) data extraction and analysis of Linux-based Android mobile devices/cell phones with open-source tools. Topics covered include data extraction from a "live" system; cell phone file systems (EXT/YAFFS) and data recovery; cell phone configuration records; Android/Linux log analysis and operating system artifacts; memory dump analysis (NAND); Android Operating System application artifacts to include SMS/MMS messaging apps, contacts list, calendar, Gmail, browser bookmarks/searches, call logs, picture/video, and GPS/maps; installed application artifacts such as Facebook, Twitter, and TikTok; cell phone network forensics; Subscriber Identity Module (SIM) card analysis; and Secure Digital (SD) card analysis. |
Wed 1:30-4p |
650.837 |
INFORMATION SECURITY PROJECTS Dahbura & Li Open to MSSI students. Permission Required for non-MSSI students. All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a team-structured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for on-line distribution. When appropriate, a project can also lead to the development of a so-called "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required for MSSI students on full-time status. |
MW 10-10:50a |
650.840 |
INFORMATION SECURITY INDEPENDENT STUDY Li Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute. |
P/F grades |
01 - Xin Li 02 - Rao Kosaraju (emeritus) 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith (use 513 for group project development) 06 - Joanne Selinski (use 513 for group project development) 07 = Harold Lehmann [SPH] 08 - Ali Madooei (use 513 for group project development) 09 - Greg Hager (ugrad research use 517, not 507) 10 - Craig Jones 11 - Sanjeev Khudhanpur [ECE] 12 - Yair Amir (emeritus) 13 - David Yarowsky 14 - Noah Cowan 15 - Randal Burns 16 - Jason Eisner (ugrad research use 517, not 507) 17 - Mark Dredze 18 - Michael Dinitz 19 - Rachel Karchin [BME] 20 - Michael Schatz 21 - Avi Rubin (emeritus) 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Rai Winslow [BME] 26 - Misha Kazhdan 27 - David Hovemeyer (use 513 for group project development) 28 - Ali Darvish (use 513 for group project development) 29 - Alex Szalay [Physics] 30 - Peter Kazanzides 31 - Jerry Prince [BME] 32 - Carey Priebe [AMS] 33 - Anjalie Field 34 - Rene Vidal [BME] 35 - Alexis Battle (ugrad research use 517, not 507) [BME] 36 - Emad Boctor (ugrad research use 517, not 507) [SOM] 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Jean Fan [BME] 45 - Liliana Florea [SOM] 46 - Casey Overby Taylor [SPH] 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dahbura (ugrad research use 517, or 513 for group project development) 50 - Joshua Vogelstein [BME] 51 - Ilya Shpitser 52 - Austin Reiter 53 - Tamas Budavari [AMS] 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Xin Jin 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh [CLSP] 60 - Mihaela Pertea [BME] 61 - Archana Venkataraman [ECE] 62 - Matt Post [CLSP] 63 - Vishal Patel [ECE] 64 - Rama Chellappa [ECE] 65 - Mehran Armand [MechE] 66 - Jeremias Sulam [BME] 67 - Anqi Liu 68 - Yana Safanova 69 - Musad Haque 70 = Stephen Walli 71 = Gregory Falco [CaSE] 72 - Thomas Lippincott [CLSP] 73 - Joel Bader [BME] 74 - Daniel Khashabi 75 - Nicolas Loizou [AMS] 76 - Alejandro Martin Gomez 77 - Kenton Murray 78 - Ehsan Azimi [SoN] 79 - Krishan Sabnani 80 - Nicholas Andrews [HTLCOE] 81 - Muyinatu (Bisi) Bell [ECE] 82 - Ziang Xiao 83 - Renjie Zhao 84 - Alex Marder 85 - Tianmin Shu 86 - Dawn Lawrie [HLTCOE]