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P. Lepin

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An excellent introduction into essential machine learning techniques. The course is very rich in content, and covers a lot of ground, but doesn't ever devolve into empty hand-waving. The course favours practical approach to machine learning, and will often skip the theory and/or underlying principles (leaving formula derivation as a purely optional exercise for those interested in this aspect of ML). Prof. Ng is obviously enthusiastic about the subject, and the course as a whole feels very polished. On the downside, the programming assignments are not very challenging and do not require any creativity, as they boil down to following very detailed instructions. The assignments remain quite instructive despite that, as there's a lot of support code meant to visualize the results and provide various statistics to help students understand how does everything work. This doesn't seem to be an oversight or anything like that, but rather conscious course design as a 'ML cookbook'. Since going through this class last spring I actually employed a few of the techniques taught in my day-to-day work, and this class was instrumental in sparkling my newfound interest for statistics. Required skills: elementary algebra, coding skills Recommended skills: first-order logic, linear algebra, probability & statistics, multivariate calculus, Octave Workload: low Difficulty: low Value: high Fun: high