Course Description
Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.
Average difficulty
Average quality
Even for a CS course the material is extremely dense and there is a great deal of math involved. But the instructors are excellent and you definitely learn a ton.
Course comprises of bi-weekly assignments. Prepare to spend 20+ hours on the assignment if you do it individually. First day of class we got an assignment that was 20 pages long, each page with multiple questions and/or a small coding portion involved. Highly recommend getting a good partner.
| Difficulty: | 5 | |
| Quality: | 5 |
The course is amazing. Great material, stimulating assignments that are moderately challenging, and great teaching. I would definitely recommend getting solid foundations in Matrix stuff (either reviewing MATH 221 material, or even taking MATH 307 (I strongly recommend this)) before taking this course. Waitlists are long, but it gets cleared almost every year, so don’t lose hope and make sure to work on A1 during the time you’re waiting!
| Difficulty: | 4.5 | |
| Quality: | 5 |
I thought it was a really good course, it teaches you about a lot about machine learning as a whole. It goes over a lot of the fundamentals but also has some focus on the more modern methods later into the course. We were able to have cheat sheets on the exam, so generally the course was a lot more focused on critical thinking as opposed to memorization. The exams and assignments felt fair, it was just a lot of work. It is very math heavy, so be comfortable with linear algebra.
| Difficulty: | 4.5 | |
| Quality: | 4.5 |