Statistical/Machine Learning

 
Semester 1 2022-2023
Tuesday 15-18, Kaplun 118
 
Lecturer: Saharon Rosset
Schreiber 022
saharon@tauex.tau.ac.il
Office hrs: By email coordination
Textbook: Elements of Statistical Learning by Hastie, Tibshirani & Friedman

Announcements and Handouts

Signup link for take home final (deadline 10 January 2023)

25 October:
Notes for class 1
Slides from class 1
r code for class 1.
Make-up class 2 for election day on 4 November:
Notes for class 2
Homework 1 (due 15 November before class). Submission in pairs is encouraged, but not in triplets or more please.
8 November:
Notes for class 3
Slides demonstrating the bias-variance decomposition in Fixed-X linear regression from a geometric perspective
Competition instructions are now available. Some code to read and examine the training data.
15 November:
Notes for class 4
Homework 2 due 6 December before class (you may find this writeup on quantile regression helpful). Submission in pairs is encouraged, but not in triplets or more please.
22 November:
Notes for class 5
R code for running regularized regression methods on Netflix dataset
29 November:
In the first part of the class we will complete the discussion of Lasso from last week's notes.
Notes on classification for class 6
R code for running classification methods on Netflix dataset
4 December: Competition week 4 update: 13 teams, 8 already in the bonus, leader still at 0.74534.
6 December:
Notes on classification for class 7 (not final)
Signup link for take home final (deadline 10 January 2023)

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 downloadable PDF of the book, data and errata)

Other recommended books:
Computer Age Statistical Inference by Efron and Hastie
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 project.
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 recommended to use R (freely available for PC/Unix/Mac).
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.
Using Python is also possible, the main downside is that the code I hand out (which in many cases is also useful for the homework) is in R.



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On 05 Dec 2022, 17:17.