What's this course about?

The course is an introductory B.Sc. level course on the topic of machine learning and deep learning. Starting with a review of probability theory and Python programming, we then introduce various machine learning approaches (supervised, unsupervised, and reinforcement learning) and tasks (regression, classification, clustering, and dimensionality reduction), using recent success examples. Topics covered include linear and logistic regression, Gaussian process, regularization, generalization and model selection, numerical optimization, principal component analysis, clustering, neural networks, and auto-encoders. In the exercise class, you will apply theoretical knowledge to practical situations and learn how to utilize machine learning tools to solve engineering and scientific problems.