An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences.
- Probability Theory (BTRY 3080, ECON 3130, MATH 4710, or strong performance in ENGRD 2700 or equivalent)
- Linear Algebra (MATH 2940 or equivalent)
- CS2110 or equivalent programming proficiency
CS 4786 focuses on Unsupervised Learning. Topics include:
- Dimensionality Reduction (PCA, CCA, Random Projection)
- Clustering (K-Means, Single-Link, Spectral)
- Probabilistic Modeling (Mixture Models, Hidden Markov Models, EM Algorithms)
6 assignments, 2 Kaggle projects, 0 prelims, 0 finals
If you have a background in ML or strong knowledge of probability, the assignments may be light, but otherwise start early just in case. Projects can be a heavy workload, but at least you have a team.
- Go to office hours for assignments
- Start early on everything, especially the projects!
- If Kaggle’s format hasn’t changed, you get a number of submissions per day. Use them!
- Even if you do great on Kaggle, don’t skimp on the writing quality of your report.
- Even though there aren’t exams, try to review the lectures so you can get the most out of the class!
- Python has a lot of awesome libraries for ML like numpy and scikit-learn.
While the class was hard for me, the format of the class was enjoyable. Even though at one point I spent two days straight locked in my room trying to implement a working Hidden Markov Model, I learned a lot about machine learning the whole time.
|Semester||Time||Professor||Median Grade||Enrollment||Course Page|
|Fall 2017||TR 11:40 AM - 12:55 PM||Karthik Sridharan||-||-||http://www.cs.cornell.edu/courses/cs4786/2017fa/|
|Fall 2016||TR 11:40 AM - 12:55 PM||Karthik Sridharan||A||92||http://www.cs.cornell.edu/courses/cs4786/2016fa/|