Machine Learning

An introductory course to machine learning, with a focus on supervised learning using linear models. The course will have four parts: (1) mathematical background on linear algebra, probability, and optimization. (2) classification methods including Bayesian decision, linear regression, logistic, regression, and support vector machine. (3) robustness of classifier and adversarial examples. (4) learning theory on the feasibility of learning, VC dimension, complexity analysis, bias-variance analysis. Suitable for senior undergraduates and graduates with a background in probability, linear algebra, and programming.


Credit Hours:


Learning Outcomes: 1.  Apply basic linear algebra, probability, and optimization tools to solve machine learning problems. 2.  Understand the principles of supervised learning methodologies, and can comment on their advantages and limitations. 3.  Explain the trade-os in model complexity, sample complexity, bias, variance, and generalization error in the learning theory. 4.  Implement, debug, and execute basic machine learning algorithms on computers.