Introduction to Machine Learning

This course provides a broad introduction to machine learning paradigms Including supervised, unsupervised, deep learning, and reinforcement learning as a foundation for further study or independent work in ML, AI, and data science. Topics include linear models for classification and regression, clustering, low rank representations (PCA), n-gram language models, matrix factorization, feedforward neural nets and convolutional neural nets, Markov decision process, planning, and reinforcement learning. Interesting applications are presented for all these models. (Not recommended for students who have already taken a 4xx AI/ML course.)