| Semester | Spring | Summer |
|---|---|---|
| Course | 605.445.31 - Artificial Intelligence | 605.445.81 - Artificial Intelligence |
| Instructor | Steve Butcher | |
| TA | None | We might have one this semester...stay tuned. |
| Time | Anytime | Mon 7-10:10 PM |
| Place | Anywhere | TBD |
| Required Textbooks |
Artificial Intelligence: A Modern Approach, 3rd Edition By Norvig & Russell. ISBN13: 978-0136042594 This is a latest edition. Some reviews have said that it is more like 2.1 than 3.0, we'll muddle through. Although I have placed the book in the online EP bookstore, buy it where you can get it the cheapest. |
|
Artificial Intelligence is a broad field covering topics ranging from game playing to localization algorithms, constraint satisfaction problems to neural networks. Many of the topics have their own courses (Neural Networks, Machine Learning, Bayesian Networks). By necessity, this will be a survey course requiring quite a bit of reading and self-study on student's part. Class time will be devoted to lectures and demonstrations covering the key points of the reading material but homework will often depend on information not necessarily provided in class.
This is a great time to take AI as we just had the 100th anniversary of the birth of Alan Turing considered to be one of the fathers of computing and artificial intelligence.
Once the semester starts, all information about the course, homework submissions, etc. will be provided through Blackboard. The class should be available the first day of the semester. You'll need your JHED and password to login.
There are weekly programming assignments in this course. This makes the course load a bit higher than average if you can't already program or don't know Python. In the "old days" you would have been learning Lisp at the same time. There are also required self-checks and class/online participation.
The required language for this course will be Python. Python is becoming the lingua franca of applied machine learning and statistics in industry through the use of the excellent NumPy, SciPy and matplotlib libraries. It has its foibles but its a serviceable language for our purposes.
You should have installed Python (around version 2.7 but not 3) on your system before the course starts. You should also have installed NumPy, SciPy, and matplotlib as well as ipython. You can install the kit and kaboodle using Enthought's Python distribution which is free for students. I have also had students install everything on an Amazon server. For getting up to speed with Python syntax, I'd recommend Learn Python the Hard Way.
Resources
If you feel like you might need a refresher in probability or linear algebra, I suggest the excellent tutorials at Khan Academy