Artificial Intelligence is a foundational pillar of modern computer science. From enabling breakthroughs in image recognition and generation to powering state-of-the-art text generation and machine translation systems, AI aims to replicate and assist human mental tasks through computational means. At its core, AI lies at the intersection of computer science, cognitive science, and philosophy, with the goal of understanding and engineering intelligent behavior in machines. Advances in AI are transforming industries across the board and changing how we interact with the world.
The power and promise of data science and AI
The AI master's track is closely aligned with the Johns Hopkins Data Science and AI Institute—a cross-disciplinary hub that provides a nexus for experts from diverse disciplines who bring data science, machine learning, and AI to bear on activities across Johns Hopkins institutions and beyond. The institute’s research delves into the foundations of data science and AI and drives applications over a wide range of areas, including national security, societal safety, materials design, public health, clinical care, neuroscience, space systems, and policy.
The Johns Hopkins University is home to several other leading research labs and centers advancing the state of the art in AI. These include the Laboratory for Computational Sensing and Robotics and its work on computer-assisted surgery and autonomous systems and the Center for Language and Speech Processing, focused on natural language understanding and speech technologies.
Students in the AI master’s track will gain deep foundational knowledge in artificial intelligence, including core areas such as machine learning, computer vision, natural language processing, and robotics. Through electives and project-based learning, students will apply this knowledge to real-world problems. Many students will have the opportunity to work directly with faculty on cutting-edge research, either through independent study or a master’s thesis.
Johns Hopkins’ strong ties to leading tech companies and research labs open up a wealth of networking opportunities, internships, and career pathways. Our graduates have joined doctoral programs at leading institutions or have gone on to work at top companies, including Google, Microsoft, Meta, Amazon, and Apple.
Requirements
The AI track is open to master’s students in the Department of Computer Science. Requirements involve taking either 5 AI courses or 4 AI courses and a master’s project/thesis in AI. At least one course must be taken from each of the following core areas:
Machine Learning:
- 601.675 Machine Learning
- 601.682 ML: Deep Learning
- 601.674 Machine Learning Theory
- 601.684 Explainable AI Design & Human-AI Interaction
Text and Speech:
- 601.665 Natural Language Processing
- 601.667 Introduction to Human Language Technology
- 601.668 Machine Translation
- 601.671 NLP: Self-Supervised Models
Vision and Robotics:
- 601.661 Computer Vision
- 601.663 Algorithms for Sensor-Based Robotics
- 530.646 Robot Devices, Kinematics, Dynamics, and Control
- 601.685 Probabilistic Models of the Visual Cortex
List of Artificial Intelligence Courses
Please note that this list is not comprehensive. Check with the Track Director or the Director of Graduate Studies for approval if there is a course that you would like to take and consider an AI course but is not listed here.
- EN.601.604 Brain & Computation
- EN.601.637 Federated Learning and Analytics
- EN.601.655 Computer Integrated Surgery I
- EN.601.656 Computer Integrated Surgery II
- EN.601.663 Algorithms for Sensor-Based Robotics
- EN.601.664 Artificial Intelligence
- EN.601.670 Artificial Agents
- EN.601.672 Natural Language Processing for Computational Social Science
- EN.601.673 Cognitive Artificial Intelligence
- EN.601.676 Machine Learning: Data to Models
- EN.601.677 Causal Inference
- EN.601.684 ML: Interpretable Machine Learning Design
- EN.601.686 Machine Learning: Artificial Intelligence System Design & Development
- EN.601.689. Human-in-the-Loop Machine Learning
- EN.601.690. Introduction to Human-Computer Interaction
- EN.601.691 Human-Robot Interaction
- EN.601.763 Advanced Topics in Robot Perception
- EN.601.764 Advanced NLP: Multilingual Methods
- EN.601.769 Events Semantics in Theory and Practice
- EN.601.770 AI Ethics and Social Impact
- EN.601.771 Advances in Self-Supervised Statistical Models
- EN.601.778 Advanced Topics in Causal Inference
- EN.601.779 Machine Learning: Advanced Topics
- EN.601.780 Unsupervised Learning: From Big Data to Low-Dimensional Representations
- EN.601.783 Vision as Bayesian Inference
- EN.601.787 Advanced Machine Learning: Machine Learning for Trustworthy AI
- EN.601.788 Machine Learning for Healthcare
- EN.601.792 Advanced Topics in Conversational User Interfaces
- EN.520.612 Machine Learning for Signal Processing
- EN.520.625 Efficient Computing for AI
- EN.520.637 Foundations of Reinforcement Learning
- EN.520.638 Deep Learning
- EN.520.640 Machine Intelligence on Embedded Systems
- EN.520.645 Audio Signal Processing
- EN.520.646 Wavelets & Filter Banks
- EN.520.647 Information Theory
- EN.520.650 Machine Intelligence
- EN.520.656 Data Smoothing Using Machine Learning
- EN.520.659 Machine learning for medical applications
- EN.520.665 Machine Perception
- EN.520.666 Information Extraction
- EN.520.680 Speech and Auditory Processing by Humans and Machines
- EN.520.698 Networks Meet Machine Learning: Methods and Applications
- EN.553.632 Bayesian Statistics
- EN.553.633 Monte Carlo Methods
- EN.553.634 Elements of Statistical Learning
- EN.553.636 Introduction to Data Science
- EN.553.639 Time Series Analysis
- EN.553.640 Machine Learning in Finance
- EN.553.653 Mathematical Game Theory
- EN.553.662 Optimization in Data Science
- EN.553.669 Large-Scale Optimization For Data Science
- EN.553.693 Mathematical Image Analysis
- EN.553.694 Applied and Computational Multilinear Algebra
- EN.553.724 Probabilistic Machine Learning
- EN.553.728 Optimal Transport
- EN.553.733 Nonparametric Bayesian Statistics
- EN.553.740 Machine Learning I
- EN.553.741 Machine Learning II
- EN.553.743 Equivariant Machine Learning
- EN.553.763 Stochastic Search and Optimization
- EN.553.764 Modeling, Simulation, and Monte Carlo
- EN.553.767 Iterative Algorithms in Machine Learning: Theory and Applications
- AS.050.603 Intro to Cognitive Neuroscience
- AS.050.626 Foundations of Cognitive Science
- AS.050.633 Psycholinguistics
- AS.050.636 Intro to Neurolinguistics
- AS.050.637 Reading the Mind: Computational Cognitive Neuroscience of Vision
- AS.050.658 Language & Thought
- AS.050.665 Cracking the Code: Theory and Modeling of Information Coding in Neural Activity
- AS.050.683 Computational Social Cognition
- AS.150.621 Advanced Topics in Philosophy of Mind and Cognitive Science
- AS.150.626 Complexity, Information, and Emergence
- AS.150.632 Formal Logic
- AS.200.617 Fundamentals of Cognitive Psychology
Example Curriculum
Year 1 Fall:
- Course selected from Machine Learning
- Course selected from Text and Speech
- 2 additional CS courses
Year 1 Spring:
- Course selected from Vision and Robotics
- Additional AI course
- Additional CS course
- Start on project/thesis
Year 1 Summer:
- Project/thesis/internship
Year 2 Fall:
- 2 additional AI/CS courses or project/thesis
- Additional AI/CS course