Prerequisites: CSCI-C243 or CSCI-C343 or INFO-I308 or INFO-C307 and CSCI-C250 or INFO-I201.

Textbooks: (reference)

• P. Norvig, S. Russell (2021): Artificial Intelligence. A Modern Approach. 4th edition, Pearson.
• I. Goodfellow and Y. Bengio (2016): Deep Learning. The MIT Press.

Other Links:
Python documentation

Grading system:

• About 10 homework assignments 20 points each
• Project (B551) 50 points
• Class Participation (up to 6) 5 points each
• Midterm exam 50 points
• Final exam 50 points

Guidelines for assignments:

• The assignments will be posted on Canvas and on the course web page and must be turned in on Canvas.
• The assignments are due at midnight of the due date.
• No homework accepted after 1 week from the due date. A late homework loses 10% of the points per day, up to 50% of the points.
• Class participation items are not announced beforehand; there is no make up for them.
• Attendance is expected.
• All of the assignments are individual. Consulting with colleagues is acceptable, but homework submissions that are too similar can be penalized or rejected. No credit will be given for homeworks obtained from external sources unless explicitly allowed.
• See extended plagiarism policy, other campus-wide policies, and calendar information in the General Campus Syllabus on Canvas in Module 1.
• Reasonable expectations concerning the program structure and clarity: functions should be commented and should not contain more than 20 lines of code. Multiple source files are expected when appropriate.

Programming environment:

• Platform: Any OS, labs NS#207 and NS#164. Access with your Crimson Card.
• Language:Python 3.7 or later
• IDE:Any recent Python IDE.

Course Topics

Definition, intelligent agents, generative AI

Logic, knowledge representation, reasoning

Fuzzy logic, probabilistic reasoning

Planning, game playing, decision-making

Machine learning

Optimization and classification algorithms such as genetic algorithms and neural networks

Elements of natural language processing.