This course will introduce some machine learning algorithms. We will cover most popular algorithms: (a) (Generalized) Linear Model and Regularization; (b) Decision Trees; (c) Support Vector Machines; (d) Neural Networks; (e) Association Rules and Cluster Analysis;(f) Boosting and Random Forests; (g) Reinforcement Learning. In order to take this course, it is better you take statistical computing in advance. Or we will focus more on GLM and MA, and skip some algorithms such as SVM and RF.

This course will introduce some machine learning algorithms. We will cover most popular algorithms: (a) (Generalized) Linear Model and Regularization; (b) Decision Trees; (c) Support Vector Machines; (d) Neural Networks; (e) Association Rules and Cluster Analysis; (f) Boosting and Random Forests; (g) Reinforcement Learning. In order to take this course, it is better you take statistical computing in advance. Or we will focus more on GLM and MA, and skip some algorithms such as SVM and RF.

Machine learning is the science of data analysis that automates a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).

Machine learning is the science of data analysis that automatically a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).

a. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
b. Machine Learning - A Probabilistic Perspective by Kevin Murphy
c. Pattern Recognition and Machine Learning by Christopher Bishop

a. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
b. Machine Learning - A Probabilistic Perspective by Kevin Murphy
c. Pattern Recognition and Machine Learning by Christopher Bishop