Academics

Machine Learning Basics and Practices

Subject No.
Research
Credit
Classification
Prerequisite
EE.20014
Communication
3
Elective

This course is designed to teach basics and practices of machine learning to 2nd year undergraduate students. Every week consists of a lecture for learning basic theories and a practice session for coding to devise and implement algorithms. Emphasis is on developing hands-on experiences in designing various machine learning methods. This is a self-contained course but basic knowledge of probabilities and linear algebra would be helpful. CS101 or equivalent background is highly desired. The course is also open to 3rd and 4th year students.

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