This module is an introductory module to Artificial Intelligence, and it is a prerequisite for most modules in the AI track. Note that unlike ML, ST2334 isn’t a prerequisite for this module, even though familiarity with basic probability theory would help significantly. Topics covered include uninformed & informed search, adversarial search, local search, CSPs, MDPs, reinforcement learning, Bayesian inference, and predicate logic.
Lectures were recorded for this semester. Under Prof. Meel, no lecture slides were used for the lectures, so it was crucial to attend the lectures or watch the webcasts in order to understand what is going on in the module. Sections of the textbook were also provided for students who were keen to read up before the lectures, albeit not compulsory. Tutorials were conducted mostly by graduate students, and were slightly more interactive than other CS modules.
There were weekly written assignments which were usually pretty straightforward, testing on students’ understanding of the lectures. There were also 3 projects, where students were randomly assigned into groups of 2. Two of these projects were based on UC Berkeley’s CS188 Pacman Projects, with slight modification. Finally, each student was assigned a lecture to scribe for, which could be done in either LaTeX or Word. Scribing for all lectures would grant you bonus of 5% for your overall grade. Midterms and finals were both open-book.
Prof. Meel’s teaching style of not using slides may not be suitable/appeal to some students, so do take a look at some of MIT’s algorithm lectures on youtube (e.g. 6.046) to see whether you are fine with it, before commiting to take the module under him.
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