Course Information: Statistics 413 Fall 2017 Tuesdays & Thursdays, 4:00  5:15pm Location: Herzstein Hall 210 Piazza Course Webpage Instructor: Genevera Allen Office: DH2080 Email: gallen@rice.edu Office Hours: Thursday 5:15  6:30pm Teaching Assistant: Tianyi Yao Office: DH2090 Email: ty13@rice.edu Office Hours: Wednesdays 45pm Recitation: Wednesdays 56pm, Mech Lab 254 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. 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 5pm to the TAs in Duncan Hall. There will be a deduction of 25% of the grade for homeworks that are not typeset using LaTeX. There will be a deduction of 25% of the grade for each day homeworks are late. 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 introduction to concepts, methods, and best practices in statistical machine learning. Topics covered include regularized regression, classification, kernels, dimension reduction, clustering, trees, and ensemble learning. Emphasis will be placed on applied data analysis. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to (i) apply basic statistical learning methods to build predictive models or perform exploratory analysis, (ii) properly tune, select and validate statistical learning models, and (iii) interpret their results.Tentative Lecture Schedule:August 22: Introduction & KNNAugust 24: MSE & Least Squares September 5: Ridge Regression September 7: Sparse Regression I September 12: Sparse Regression II September 14: Model Validation I September 19: Model Validation II September 21: GLMs I September 26: GLMs II September 28 & Sparse GLMs & Bayes Classifiers October 3: LDA October 5: SVMs I October 12: SVMs II October 17: Intro to NonLinear I October 19: Intro to NonLinear II October 24: Dimension Reduction I October 26: Dimension Reduction II October 31: Clustering I November 2: Clustering II 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
