|Pre-Requisites:||S681 Topics in Machine Learning I or STAT-S722 or equivalent or Permission of instructor|
|Contact Person for Authorization:||Daniel McDonald|
|Days Per Week Offered:||2|
|Recommended follow-up classes:||STAT-S781, STAT-S722-722, STAT-S675|
|Syllabus:||No Syllabus Avaliable|
|Keywords:||Machine learning, data mining, big data, statistical learning, regression, classification, nonparametrics, optimization|
|Description:||This two-semester course sequence emphasizes supervised machine learning techniques.
Strong emphasis is given to the theoretical and statistical aspects of the methods covered. In addition, practical aspects of methodology, the underlying concepts and intuition behind each method, and computational considerations are addressed. The goal is to provide a balance between the "art" of designing good learning algorithms and the "science" of analyzing an algorithm's statistical properties and performance guarantees.
The sequence will provide students with a deep understanding of the blend of methodological and computational concerns with statistical theoretic analysis. It will also allow students to select methods appropriate to problems in their own research.
|Books:||Hastie, Tibshirani, and Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction|
|Substantive Orientation:||The emphasis is on statistical methodology as it applies broadly|
|Statistical Orientation:||Methodology and theory with example applications|
|Applied/Theoretical:||The focus is on various statistical techniques and some of the theory used to justify their application|
|Formal Computing Lab:||No|
|How Software is Used:||For homework sets and projects|