EECS 592: Artificial Intelligence Foundations

Instructors:

• Professor Lu Wang
• GSI Frederick Zhang
• GSI Kaijian Zou

Time and Location:

• Lectures: Mondays and Wednesdays, 9-10:30am, DOW 1013 [Lu Wang]
• Discussions: Fridays, 9:30-10:30am, DOW 1010 [Frederick Zhang, Kaijian Zou]

Staff and Office Hours:

• Prof. Lu Wang, Wednesdays 4:15pm-5:15pm, BBB 3812
• GSI Frederick Zhang, Mondays 4:30pm-5:30pm, BBB 1637
• GSI Kaijian Zou, Thursdays 1pm-2pm, BBB 1637

Discussion Forum: Piazza, sign up at piazza.com/umich/fall2024/eecs592

Course Description

The purpose of this course is to provide a broad introduction to the foundational ideas and techniques of Artificial Intelligence, as well as to develop an appreciation for the engineering issues underlying the design of intelligent computational agents. The successful student will finish the course with specific modeling and analytical skills (e.g., search, logic, probability), knowledge of many of the most important knowledge representation, reasoning, and machine learning schemes, and a broad understanding of AI principles and practice. The course will serve to prepare the student for further study of AI, as well as to inform any work involving the design of computer programs for substantial application domains.

Syllabus, schedule, assignments, and other course-related materials are available on Canvas.

Textbook and References

• Main Textbook:

  • Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach, Fourth Edition".

• Other References:

  • Christopher Bishop, "Pattern Recognition and Machine Learning".
  • Dan Jurafsky and James H. Martin, "Speech and Language Processing, 2nd Edition".
  • Third edition draft is available at web.stanford.edu/~jurafsky/slp3/.

Prerequisites

Graduate standing, and the equivalent of EECS 281 and its prerequisites. We assume programming experience and knowledge of programming language concepts, and familiarity with algorithmic concepts such as graph search and computational complexity. We also assume background in concepts typically acquired in a discrete mathematics course, such as logic, graphs, discrete probability, sets, counting, computational complexity (big-O), and proof techniques. We will not hesitate to employ mathematics where appropriate.

Grading

Each assignment or report is due by the end of day on the corresponding due date (i.e. 11:59pm, EST). Canvas is used for electronic submission. Assignment or report turned in late will be charged 20 points (out of 100 points) off for each late day (i.e. every 24 hours). Each student has a budget of 8 days throughout the semester before a late penalty is applied. You may want to use it wisely, e.g. save for emergencies. Each 24 hours or part thereof that a submission is late uses up one full late day. Late days are not applicable to final presentation. Each group member is charged with the same number of late days, if any, for their submission. There is no need to inform the instructors if late days are used; timestamp of the last submission on Canvas will be used for automatic grade calculation.

Grades will be determined based on homeworks, quizzes, a course project, an exam, and participation:

• Homeworks (44%): four assignments, each of 11%
• Lecture quizzes (9%): eleven quick in-class tests, each of 1%; two with lowest grades will be dropped, and no make-up
• Discussion quizzes (3%): eight quick in-class tests, each of 0.5%; two with lowest grade will be dropped, and no make-up
• Project (16%): team of 2 to 3 students, proposal (2%), reports (4%+6%, mid-term progress and final), presentations (4%, with 1% as bonus if selected as best project by peer students)
• Midterm exam (25%): open-book
• Participation (3%): participating in-class discussions, interaction on Piazza (answering questions, sharing notes, etc)