6.4110 / Representation, inference and reasoning in AI and 16.420 / Planning under uncertainty (FA23)

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Course Description

An introduction to representations and algorithms for artificial intelligence. Topics covered include: constraint satisfaction in discrete and continuous problems, logical representation and inference, Monte Carlo tree search, probabilistic graphical models and inference, planning in discrete and continuous deterministic and probabilistic models including MDPs and POMDPs.

First Meeting

The first meeting of the class will be Wednesday, September 6, 9:30-11:00 AM, in 4-370.

Please note that the undergraduate subject (6.4110) and the graduate subject (16.420) meet concurrently.

Announcements

All announcements and communication will occur over Piazza. Please sign up.

Final Exam Practice

Fall 23 final practice -- with solutions PDF
Fall 22 final -- without solutions PDF
Fall 22 final -- with solutions PDF
Fall 21 final -- without solutions PDF
Fall 21 final -- with solutions PDF

Please also review the midterm exams and midterm practice problems posted below. Note that you will responsible for First-Order Logic and HMM from these midterms/practices for the final exam.

Practice Midterm

Note that some material covered in Fall 22 has not yet been covered this term. In particular First Order Logic (FOL) and Hidden Markov Models (HMM).

Fall 23 practice -- without solutions PDF
Fall 23 practice -- with solutions PDF
Fall 22 midterm -- without solutions PDF
Fall 22 midterm -- with solutions PDF
Fall 21 midterm -- without solutions PDF
Fall 21 midterm -- with solutions PDF

Midterm solutions

Fall 23 midterm solutions - PDF