Statistical/Machine Learning

 
Semester 1 2014-2015
Monday 16-19, Kaplun 118
Home page on http://www.tau.ac.il/ ∼ saharon/StatLearn.html
Lecturer: Saharon Rosset
Schreiber 022
saharon@post.tau.ac.il
Office hrs: Thursday 16-18 (with email coordination)
Textbook: Elements of Statistical Learning by Hastie, Tibshirani & Friedman

Announcements and handouts

(27 October) Slides from class 1 and R code I demonstrated in class. You can also get just the raw code.
(3 November) Homework 1 is now available. Due 24/11 in class. Submission in pairs is encouraged (but not in triplets or larger, please).
(10 November) Competition instructions are now available. Associated code demonstrated in class.
Slides on bias-variance decomposition of linear regression.
(21 November) Competition week 2 update: so far we have (only) five teams submitting, none in the bonus but the leader is very close at 0.7707.
(24 November) Code for running regularized linear regression and PCA on competition data.
(25 November) Homework 2 is now available. Due 15/12 in class. Submission in pairs is encouraged (but not in triplets or larger, please).
The homework relies on this short writeup on quantile regression.

Syllabus

The goal of this course is to gain familiarity with the basic ideas and methodologies of statistical (machine) learning. The focus is on supervised learning and predictive modeling, i.e., fitting y ≈ f(x), in regression and classification.
We will start by thinking about some of the simpler, but still highly effective methods, like nearest neighbors and linear regression, and gradually learn about more complex and "modern" methods and their close relationships with the simpler ones.
As time permits, we will also cover one or more industrial "case studies" where we track the process from problem definition, through development of appropriate methodology and its implementation, to deployment of the solution and examination of its success in practice.
The homework and exam will combine hands-on programming and modeling with theoretical analysis. Topics list:

Prerequisites

Basic knowledge of mathematical foundations: Calculus; Linear Algebra; Geometry
Undergraduate courses in: Probability; Theoretical Statistics
Statistical programming experience in R is not a prerequisite, but an advantage

Books and resources

Textbook:
Elements of Statistical Learning by Hastie, Tibshirani & Friedman
Book home page (including data and errata)

Other recommended books:
Modern Applied Statistics with Splus by Venables and Ripley
Neural Networks for Pattern Recognition by Bishop
(Several other books on Pattern Recognition contain similar material)
All of Statistics and All of Nonparametric Statistics by Wasserman

Online Resources:
Data Mining and Statistics by Jerry Friedman
Statistical Modeling: The Two Cultures by the late, great Leo Breiman
Course on Machine Learning from Stanford's Coursera.
The Netflix Prize competition is now over, but will still play a substantial role in our course.

Grading

There will be about four homework assignments, which will count for about 30% of the final grade, and a final take-home exam. Both the homework and the exam will combine theoretical analysis with hands-on data analysis.
We will also have an optional data modeling competition, whose winners will get a boost in grade and present to the whole class.

Computing

The course will require extensive use of statistical modeling software. It is strongly recommended to use R (freely available for PC/Unix/Mac) or its commercial kin Splus.
R Project website also contains extensive documentation.
A basic "getting you started in R" tutorial. Uses the Boston Housing Data (thanks to Giles Hooker).
Modern Applied Statistics with Splus by Venables and Ripley is an excellent source for statistical computing help for R/Splus.



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On 26 Nov 2014, 21:04.