91.420/91.543 Artificial Intelligence, Fall 2014
Prof. Fred Martin,
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MWF, 10:00a10:50a, OS401
We will be using the following book:
What is AI?
There are few areas in computer science as broad as artificial intelligence. AI encompasses algorithm design, agents and robots, natural language understanding, expert systems, music and sound, as well as philosophical questions about the nature of consciousness!
One organizing principle across the field is the formalization of real-world problems based on utility functions, and then the use of various techniques to maximize expected utility. Based on this unifying theme, we will study:
- game-playing, including adversarial and stochastic search
- probabilistic reasoning, including Markov Decision Processes (MDPs) and Hidden Markov Models (HMMs)
- reinforcement learning, including value iteration and q-learning
Ideas will be developed theoretically and with practical programming challenges using the U.C. Berkeley Pac-Man projects, in which you program a progressive series of challenges inspired by the original Pac-Man arcade game.
These problem sets teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underlie real-world application areas such as natural language processing, computer vision, and robotics (see http://ai.berkeley.edu/project_overview.html).
Coding will be done in Python, which you will learn during the class if you don't know it yet.
A big part of AI is not just knowing a collection of approaches, but understanding which approach is right for which problem. To encourage the development of these skills, there will be a significant semester project where you will apply ideas from the course to a problem of your own choosing.
The textbook, as shown above, is Artificial Intelligence: A Modern Approach, by Russell and Norvig. The book is comprehensive, is well-written in the most exemplary manner, and has detailed bibliographies for each chapter. It is by far the most popular book used to teach AI, and it is also the best.
The recommended book is the third edition. New and used copies are available at the UML bookstore, or online (3rd edition: alibris; amazon; bookfinder). It would be acceptable to get a used second edition (alibris; amazon; bookfinder). Do not use the first edition.
There are five categories of work that will be assessed. These are:
- Programming assignments 20%. The assignments are the primary way for developing an understanding of how to put AI theory into practice. Assignments will lose 10% of their value per day that they are late up to a maximum of 50% off. Assignments are due before class of the day that they are assigned.
- Literature review assignment 5%. You will read two papers related to an area of AI in which you are interested, and prepare conference-style reviews of the papers.
- Two mid-semester exams 10% each x 2 = 20%.
- Final exam 25%.
- Term project 25%. A significant part of the class will be an independent implementation project, which you will specify and carry out, primarily over the last month of the semester. We'll start conceptual work on the project earlier than that. I will expect the project to represent a significant work effort.
- Classroom and discussion forum participation 5%.
Undergraduate Project Sequence
For undergraduates, 91.420 Artificial Intelligence can be grouped with 91.421 Machine Learning or 91.450 Robotics I to form a course pair.
For graduate students, 91.543 is in Group III, Human-Computer Interaction, Visualization, Robotics and AI.
Collaboration and Academic Integrity Policy
You are welcome to discuss ideas in the class with your peers. However, pair programming or other side-by-side work that involves sharing of code is not allowed. By turning in an assignment, you attest that you have written the new code that it includes. Please be familiar with the university's academic integrity policies: for undergraduates for graduate students
Discussion Group / E-Mail List
We will use Google Groups for announcements and discussion of material outside of class. Join the group by entering your email address in the box below, and following the subsequent confirmation instructions.
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