|Course Code||CS 360|
|Course Name||Foundations of Machine Learning|
|Pre-requisites||CS230 Probability and Statistics for CS|
1. Machine Learning, T. Mitchell, 2009.
1. Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, 2017. 2. Pattern Recognition and Machine Learning, C. Bishop, 2010. 3. Foundations of Data Science, M. Blum, J. Hopcroft, and R. Kannan, 2018. 4. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, H. Wickham and G. Grolemund, 2016.
|Description||Objective: To equip students to apply machine learning methods to real-world applications such as recommender systems, computer vision, bioinformatics, and text mining.
Contents: Data science basics, how to wrangle, visualize, and analyze data, using models to explore your data
Supervised learning, linear and logistic regression, generative learning, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation, support vector machines, artificial neural networks and deep learning.
Unsupervised learning, mixture of Gaussians, EM algorithm, autoencoders
Bias-variance tradeoff, regularization and model selection
Dimensionality reduction, principal component analysis, singular value decomposition