STAT W4400

6 homeworks with math and code. 2 exams with a cheatsheet.

5 or so p sets, which range from stupid easy to pretty hard. Mix of theoretical and coding. One midterm, which isn't bad, has a large curve. The final is the same.

A fun class in machine learning. We covered a lot of basic algorithms that are important to machine learning, and we did it through a statistical lens, learning a lot about statistical modelling. One of the goals was building intuition for machine learning, and I would say it was achieved successfully. However, at times, I felt this came at the cost of skipping formal proofs. Although the algorithms seem to work in reasonable cases, there is never a real definition of the learning problem we are trying to solve. In this way, the classes seemed more about applying machine learning algorithms than proving their correctness. The homeworks were reasonable, each had some math and some programming. We worked in R, and it was fairly easy to translate mathematical notation into code. The first exam was extremely challenging, the second was very easy. Presumably professor Cunningham is still trying to strike a balance there. You need to know linear algebra to take this course. Some statistics and programming wouldn't hurt either.

Excellent professor. She is engaging, high energy, and knowledgeable. I would take any class with her. Her slides are excellent and comprehensive, but you'll definitely miss a level of understanding if you don't go to class. Her assignments and lectures are a nice blend of theoretical and applied. A friend of mine once asked her about debugging and the next class she went up to my friend personally and gave them a paper on debugging in R. She is that personable, and she really cares. I can't say enough good things about her.

5 homeworks (if you use the allotted two weeks, then it is light. Otherwise it is heavy), 20% 1 midterm, 40% 1 non cumulative final, 40%

This class was easily one of my favorite classes I have taken at Columbia. As an undergrad who is not in the statistics department, I sometimes had to play some catch up on some of the statistical background, but it was totally worth it. Cunningham is, in my view, the ideal Columbia professor. He expects a lot of his students, he won’t be easy on you, he doesn’t put up with student bullshit (turning in homework late, cheating on exams, etc.), but is extremely good at teaching the material, and always happy to answer questions. He is also insanely hot. The pace of the class is heavily dictated by students asking questions to slow him down. For the first 3 weeks or so, people wouldn’t raise their hands when Cunningham stopped for questions, so he would move on assuming we were ready to go. Once people got comfortable asking questions in a large classroom, the pace slowed, he reviewed stuff that people were confused about, and things hit their stride. He is extremely good at explaining the concepts of the various algorithms through examples, diagrams, demonstrations, etc. As such, one walks away with a very good understanding of what the pros and cons of various algorithms are. His slides are quite detailed, and if you take the time to understand everything on there you will be golden. He supplements the slides with hand written examples and notes when necessary. This isn’t a pro…

Homework - Mixture of theory questions and programming in R. Due every two weeks or so, get started on them early! Midterm - Few problems with multiple parts. Final - Same as midterm, but longer.

Cunningham is a pretty good professor. His explanations are generally very clear, and he cracks some jokes occasionally. One thing that isn't so hot is how he is so unresponsive to emails and has half an hour office hours right after lecture. Just be sure to look over the slides and ask questions in class - he is very open to questions. Be sure that you are comfortable with linear algebra, calculus III, probability and statistics (at least Linear Regression Models), and programming in R. Homework 0 is the homework that Cunningham gives to see if you can hack the course or not. It's also a good idea to pick up LaTeX to format your homework - otherwise, you'll have to scan everything to submit on CourseWorks. Be warned that a good portion of the class comprises of Stats MA students, so you must do significantly better than the average (which is usually pretty high) to get an A. Otherwise, the average is curved to a B+.

6 homework 1 midterm 1 final Midterm and final will have questions that make you run through algorithms, describe the characteristics of certain techniques, and do some fancy math to prove certain qualities about these techniques - qualities not covered in class. There's no programming questions on either midterm or final so questions rely heavily on calculus and linear algebra. Both of them weren't easy, but at least the questions were fun.

Fun class, manageable workload, great professor, interesting topics and no textbook. He really encourages people to ask questions in class so do take advantage of his willingness to answer questions, no matter what other people think of them. Do learn some linear algebra and R beforehand. All programming assignments are done in R. As well, he strictly requires people to hand in pdfs for written assignments, and since it uses a lot of math, it's worthwhile to learn LaTeX. Also, be aware that this class is usually populated with graduate students mainly from the Statistics Department, so if the midterm or final is heavy in math and stats calculations or proofs, you will need to nail those tests or else get beaten by the curve. Also, TAs are helpful and often provide hints on Piazza, so look forward to that.