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Lab accounts

Accounts at the Computer Science Undergraduate Lab (CSUG) should already be set up for all pre-enrolled students over the summer, and for newly registered students by the end of the add-drop period. If you do not have an account but need one sooner, please submit a request. You must authenticate your account before you can start using it. This will also grant you card access to the lab and to the MSDNAA software library for downloading software.

Accounts on CMS should also be set up for all pre-enrolled students. If you do not have an account, please contact the head TA and ask to be added.

Prerequisites

This course has no prerequisites other than familiarity with basic data structures and programming (e.g., CS2110) and the basic mathematical skills obtained in CS2800. An understanding of inference in prepositional logic and basic blind search techniques (i.e., breadth-first and depth-first search) is also assumed, but background readings in these topics can be provided for those with a deficiency in this area.

If you have not taken prerequisites but want to take this course anyway, please be aware that there is a significant programming component. Get a copy of Introduction to Algorithms (Cormen et al.) and read and understand the chapters on Elementary Data Structures, Hash tables, Binary Search trees, and Elementary Graph algorithms. Do a few of the exercises to test your knowledge You must be comfortable programming in some high level language.

Syllabus

This course introduces the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics to be covered include the following:

• Introduction of AI and background: What is AI? Related fields.
• Problem solving by search: principles of search, uninformed (“blind”) search, informed (“heuristic”) search, and local search.
• Constraint satisfaction problems: definition, search and inference, and study of structure.
• Adversarial search: games, optimal strategies, imperfect, real-time decisions.
• Logical agents: propositional and first order logic, knowledge bases and inference.
• Uncertainty and probabilistic reasoning: probability concepts, Bayesian networks, probabilistic reasoning over time, and decision making.
• Learning: inductive learning, concept formation, decision tree learning, statistical approaches, neural networks

Note: some of the topics listed above may be covered only briefly, depending on time. Detailed reading information will be provided in the lectures notes and homework assignments.

Grading This is a 3-credit course. Grades will be determined based on a midterm, a final exam, homework assignments, and class participation.

• 15%: Midterm
• 35-45%: Final Exam (percentage depends on participation, see below)
• 40%: Homework (~6 assignments)
• 0-10%: Participation (optional, see below)

The midterm is an in-class examination (Friday, Oct 7).

The final will be given on the day and time slot scheduled by the university. Please check before you schedule your trip back home for winter break.

Homework assignments are or will be available below and on CMS. Assignments are due in hardcopy in class as well as on CMS. Graded homework assignments and the midterm can be picked-up in Upson 360.

Cooperation on homework assignments. Each student must solve problems and write their own code independently. While students are encouraged to discuss approaches, student must not compare or copy code or jointly write code, or copy it from another source. Students aware of a circulating or posted solution to a homework question must immediately report it to the instructor.

Late submissions. All assignments are due at the beginning of class on the due date. Assignments turned in late will drop 10% for each period of 24 hours for which the assignment is late (including weekends). If you turn in your assignment late, please bring a hardcopy to the head TA. No assignments will be accepted after grading is completed and the grades have been posted on CMS. To accommodate unforeseen situations, use slack days: Each student is allowed to submit an assignment a day late three times (or three days late, once, etc.) without any explanation, for any reason (e.g. sickness, religious, or any personal reason). Please use slack days judicially. Mark the number of remaining slack days at the top of your submission.

Re-grading: Although you are encouraged to talk with any TA about the questions, only the head TAs may award back points (or detract points if they find new things wrong) if you want a re-grade.

Participation: Participation in this course is not mandatory but encouraged. Students that actively participate (by using the clicker) can optionally substitute some of their final exam grade with their participation grade (this will be done automatically to maximize the grade). Participation grade is based on the proportion of class questions you responded to with your clicker. It does not matter if you answered correctly or incorrectly.

Tentative Schedule (dates tend to shift during the semester)