Learner Reviews & Feedback for Machine Learning: Regression by University of Washington

YEAHH

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做中学

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Interesting course. However, I have some mixed feelings:

I have a BS in mathematics, in Mexico (a "licenciatura", which is just between "BS" and "MS")

So, I'd say I have pretty good knowledge of statistics. So, now it is "training" instead of "fitting". It's "overfitting" instead of "multi colinearity". There are some algorithms to remove/add features (Ridge/Lasso), which -as noted- induce bias in the parameters. However, more "formal" methods susch as stepwise regression and bayesian sequences, are completely ignored.

That'd be fine except for the fact that there not even the slightest attempt to approach statistic significant, neither for the model nor for the individual parameters.

Some other methods (moving averages, Henderson MA, Mahalanobis distances) should also be covered.

So, in summary, an interesting course in the sense that ti gives an idea as to where lies the state of the art, but a little bit disappointing in the sense that -except for some new labels for the same tricks, and a humongous computing power- there is still nothing new under the sun. Still, worth the time invested

An excellent and quite extensive foray into regression analyses from single-variable linear regression to nearest-neighbor and kernel regression techniques, including how to use gradient vs. coordinate descent for optimization and proper L1 and L2 regularization methods. The lecture slides have some questionable pedagogical and aesthetic qualities, and they could use some more polish from someone who specializes in teaching presentation methods, but the meat of the course comes from its quizzes and programming assignments, which are well split between practical use (via Graphlab Create and SFrame) and a nuts-and-bolts assignment that have you implement these methods from scratch. An extremely valuable course for someone who wants to use these for a data science application but also wants to understand the mathematics and statistics behind them to an appreciable degree.

The only complaint I have is that the programming exercises were not challenging enough. The lecture videos were great to build up an understanding from fundamentals, but the assignments did not fully test the concepts. There were too many exercises that were fill-in-the-blank with most of the code already written. I would appreciate more rigorous programming exercises to facilitate an in-depth understanding of the topics. Moreover, the programming exercises were not applicable to real-world applications because all the data was already neatly presented and the desired outcome was known ahead of time. In order to mimic real-world machine learning problems, we should be required to clean the data and answer open-ended questions that require exploring and understanding the data before developing machine learning models to extract usable information.

Courses like this are always difficult to judge because of the great variety of students coursera reaches. That is, some class members finished this course in the first week it was open, others still struggled till the last minute. For some the math was too simply, for others the python programming was too confusing. All in all it strikes a reasonable balance between novice learners and more advanced students.

What the course could stand to really benefit from is some kind of repository of code, for those students who successfully completed the assignments to compare to their own. It seems pretty clear that there are some advanced python users whose insights could help improve one's coding skills.