This course will introduce some machine learning algorithms. We will cover very basic popular algorithms: (a) (Generalized) Linear Model and Regularization; (b) Decision Trees; (c) Neural Networks; (d) Association Rules and Cluster Analysis;(f) Dimension Reduction. Depending on the progress, some advanced topics such as Support Vector Machines, Boosting, or Random Forests might be introduced or omitted.

This course will introduce some machine learning algorithms. We will cover very basic popular algorithms: (a) (Generalized) Linear Model and Regularization; (b) Decision Trees; (c) Neural Networks; (d) Association Rules and Cluster Analysis; (f) Dimension Reduction. Depending on the progress, some advanced topics such as Support Vector Machines, Boosting, or Random Forests might be introduced or omitted.

Machine learning is the science of data analysis that enables computers to learn without being explicitly programmed. From the computer science point of view, unlike computational statistics dealing with prediction-making or data mining focusing on data-exploring, machine learning uses data to iteratively detect patterns and adjust models accordingly. This introductory course provides students an overview of the field of machine learning, as well as of its fundamental concepts and algorithms from practical perspective. Usually, machine learning algorithms are categorized as being supervised or unsupervised. Some of the important topics include (1) Supervised learning (Linear and Logistic Regressions, Classification and Regression Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Others (Boosting and Random Forests).

Machine learning is the science of data analysis that enables computers to learn without being explicitly programmed. From the computer science point of view, unlike computer statistics dealing with prediction-making or data mining focusing on data-exploring, machine learning uses data to iteratively detect patterns and adjust models accordingly. This introduction course provides students an overview of the field of machine learning, as well as of its fundamental concepts and algorithms from practical perspective. Usually, machine learning algorithms are classified as being supervised or unsupervised. Some of the important topics include (1) Supervised learning (Linear and Logistic Regressions, Classification and Regression Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Others (Boosting and Random Forests).

a. An Introduction to Statistical Learning with Application in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
b. Machine Learning with R by Brett Lantz
c. Applied Predictive Modeling by Max Kuhn and Kjell Johnson
d. Pattern Recognition and Machine Learning by Christopher Bishop

a. An Introduction to Statistical Learning with Application in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
b. Machine Learning with R by Brett Lantz
c. Applied Predictive Modeling by Max Kuhn and Kjell Johnson
d. Pattern Recognition and Machine Learning by Christopher Bishop