Machine Learning Basics and Practices

This course is designed to teach basics and practices of machine learning to 2^nd 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 3^rd and 4^th year students.