CS 427/527 Class Syllabus

Course Web Page

Course Description

Prerequisites

• Expertise in programming at the level of CS351 or equivalent.
• Mathematical and Algorithmic maturity at the level of CS361/362 or equivalent. For example, you should be familiar with trees, graphs, asymptotic notation, probability and basic proof techniques.

Assignments:

• Assignment deadlines are strict: late projects and homeworks will automatically receive a grade of zero, without prior approval. Prior approval is generally given only in the case of a medical problem or family emergency.
• Group collaboration is encouraged on homeworks and projects, provided that you write at the top of your homework the names of all the other students that you collaborated with. Note that although collaboration is encouraged, the solutions must always be written up individually. You should not look at or copy another student's solution and should not copy solutions from the Internet. In particular, when writing up your solutions, you should not be looking at any other solution. A rule of thumb here is the ''Star Trek'' Rule. After working with your group, go watch a half hour of Star Trek on TV, or your favorite mindless (sorry Trekkies) but fun TV show, before you write up the solutions. You may consult other textbooks or the Internet as you would another student (i.e. cite your source and use the ``Star Trek'' rule).
• Remember Copying solutions from another student or from the Internet is cheating. In case a student presents a solution that is essentially identical in whole or in part to solutions from another student or other source, that student will receive a 0 on the assignment, may be reported to the University Administration and may not be permitted to continue in the class.
• Put pages of hw in order. We don't care what order you solve the hw in, but before you turn it in, you must put the problems in order (this makes grading much easier)
• Staple hws, do not use paper clips, folding, tape, putty, gum, etc. Prof Saia and the TA do not bring staplers to class, so make sure you staple the hw before class (stapling helps us keep together all pages of your hw)
• Regrades: if you feel a mistake was made grading your hw, you may submit it for a regrade within one week of receiving the graded assignment. Please note as specifically as possible where you think an error was made in the grading. Since a regrade considers the entire assignment, your grade on the assignment may go up or down after the regrade.

Notes on Grading Hws

• Clarity: Make sure all of your work and answers are clearly legible and well separated from other problems. If we can't read it, then we can't grade it. Likewise, if we can't immediately find all of the relevant work for a problem, then we will be more likely to grade only what we see at first.
• Completeness: Full credit for all problems is based on both sufficient intermediate work (the lack of which often produces a 'justify' comment) and the final answer. There are many ways of doing most problems, and we need to understand exactly how YOU chose to solve each problem. Here is a good rule of thumb for deciding how much detail is sufficient: if you were to present your solution to the class and everyone understood the steps, then you can assume it is sufficient.
• Succinctness: The work and solutions which you hand-in should be long enough to convey exactly why the answer you get is correct, yet short enough to be easily digestible by someone with a basic knowledge of this material. If you find yourself doing more than half a page of dense algebra, generating more than a dozen numeric values or using more than a page or two of paper per problem for your solution, you're probably doing too much work. Don't turn in pages with scratch work or multiple answers - if you need to do scratch work, do it on separate scratch paper. Clearly indicate your final answer (circle, box, underline, etc.). Note: It's usually best to rewrite your solution to a problem before you hand it in. If you do this, you'll find you can usually make the solution much more succinct.

Topics

• Agents and Search
• A* Search and Heuristics
• Constraint Satisfaction Problems
• Game Trees: Minimax, Expectimax and Utilities
• Markov Decision Processes
• Reinforcement Learning
• Bayes Nets (Representation, Independence, Inference and Decision Networks)
• Decisions Networks
• Markov Models and Hidden Markov Models
• Machine Learning: Naive Bayes, Perceptrons and Deep Learning

Course Assessment

• Programming Projects, 25%
• Electronic Assignments, 15%
• Midterm, 25%
• Final, 35%

Grading Policies