Buckle up -- this deep, mathematically rigorous dive into the major areas of machine learning is fast-paced and challenging. In fact, most of the course is less about machine learning than the math behind it -- problem-solving and applications take a back seat to the underlying mathematical techniques. You won't see very many implementation examples or, in the programming projects, watch a machine get smarter. For that context, you either need prior ML training or to have taken the first course in the AI MicroMaster series, Artificial Intelligence with Ansaf Salleb-Aouissi, which treats most of the topics in this course at a higher, more introductory level. Also, considerable fluency in probability and statistics is assumed -- at the level of MITx 6.041, for example. Although no textbook is suggested, I found "The Elements of Statistical Learning" by Hastie et al. to be quite useful. The topics covered are numerous, too many to list without putting you to sleep, but they span all of the common machine learning techniques except neural networks, which is a subject in itself (and is nicely covered at a high level in the AI course). There are four programming projects, weekly quizzes, and a final exam that counts for a whopping 45% of your grade. Prof. Paisley's lectures are clear and deftly unpack difficult mathematical principles and derivations, although he speaks a bit slowly and I found that playing the lectures at 1.25x speed delivered a more natural cadence. All in all this is a great course, but be forewarned.
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