Course Information: Statistics 613 Fall 2017 Tuesdays & Thursdays, 10:50am  12:05pm Location: DH 1042 Piazza Course Webpage Instructor: Genevera Allen Office: DH2080 Email: gallen@rice.edu Office Hours: Thursday 5:15  6:30pm Instructor: Frederick Campbell Office: DH2077 Email: frederick.campbell@rice.edu Office Hours: Tuesdays 121pm Teaching Assistant: Minjie Wang Office: DH2125 Email: mw50@rice.edu Office Hours: Mondays 34pm Recitation: Wednesdays 56pm, DH 1046 Syllabus: [pdf] Course Schedule: [pdf] Recommended Textbooks: Elements of Statistical Learning by Hastie, Tibshirani & Friedman. Introduction to Statistical Learning by James, Witten, Tibshirani & Hastie. Statistical Learning with Sparsity by Hastie, Tibshirani & Wainwright. Statistics for HighDimensional Data by Buhlmann & van de Geer. Convex Optimization by Boyd & Vandenberghe. Grading Policy: Homeworks 35% Midterm Exam 20% Final Project / Competition 40% Class Participation 5% Homework Policy: A hard copy of homeworks are due in class or by 12pm to the TA in Duncan Hall. All homeworks must be typeset using LaTeX and no late homeworks will be accepted. Homeworks may be discussed with classmates but must be written and submitted individually. Midterm Exam: There will be an (open book / open notes) inclass midterm exam on November 7. Final Project & Contest: The final project will be a data analysis contest. The competition will begin on September 19 and can be done in teams of two people. Competition Description: [pdf] Important Deadlines: September 8: Add Deadline October 6: Drop Deadline 
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Course DescriptionThis course is an advanced survey of statistical machine learning theory and methods. Emphasis will be placed methodological, theoretical, and computational aspects of tools such as regularized regression, classification, kernels, dimension reduction, clustering, graphical models, trees, and ensemble learning. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, their mathematical and statistical properties, how to compute each method and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to (i) apply sophisticated statistical learning methods to build predictive models or perform exploratory analysis, (ii) evaluate methods for a mathematical, statistical and computational perspective, and (iii) properly validate statistical learning models and interpret their results.Tentative Lecture Schedule:August 22: Intro & MSE / Least SquaresAugust 24: Ridge Regression September 5: Sparse Regression I September 7: Sparse Regression II September 12: HighDimensional Theory I September 14: HighDimensional Theory II September 19: GLMs & Regularized GLMs September 21 & Bayes Classifiers September 26: LDA September 28: SVMs I October 3: SVMs II October 5: NonLinear I October 12: NonLinear II October 17: Model Validation I October 19: Model Validation II October 24: Dimension Reduction I October 26: Dimension Reduction & Clustering October 31: Clustering II November 2: Graphical Models November 7: InClass Midterm Exam November 9: Trees & Bagging November 14: Random Forests November 16: Boosting November 21: Boosting & Ensemble Learning November 28: Competition Presentations November 30: Competition Presentations Assignment Schedule
