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Background Knowledge Expected
This class was tough.
Professor is funny. Course material is solid and well-developed, catering to the industrial need. Recommended to all who want to work on data analytics in the future.
Covers the theory of bias-variance trade-offs and cross-validation in supervised learning, and bootstrap, shrinkage, random forests, SVMs and clustering methods.
Hours per week:
Advice for students:
Need solid mathematical background in calculus. Basic programming skills are recommended. Would be great if you have used R in the past.