Elements of Statistical Learning by Hastie,
Tibshirani & Friedman
Announcements and handouts
(13 March)Slides
from class 1 and
R
code from class. You can also get
just
the raw code. (20 March)Homework
1 is now available. Due 3/4 in class. Submission in pairs is
encouraged (but not in triplets or larger, please). (27 March)Competition
instructions are now available.
Associated
code demonstrated in class. Slides
on bias-variance decomposition of linear regression. (3 April)Code
for running regularized linear regression and PCA on competition
data. Homework
2 is now available. Uses
this
writeup on quantile regression. Due 1/5 in class (updated from 24/4!). Submission in
pairs is encouraged (but not in triplets or larger, please). Update: Since there is no class on 1/5, due date for HW2 was postponed again to 8/5
(8 May)Code
for running classification methods on competition
data. Code
for running logistic regression on South African Heart dataset. Homework
3 is now available, due 29/5 in class. Updated on 19/5! (15 May)Code
for running tree-based methods on competition
data.
(19 May) Material from extra class: Wallet estimation case study Note on Poisson regression and variance stabilization KDD-Cup 2007 case study Yehuda Koren's presentation on $1M Netflix competition
***Updated Homework 3, with a problem on these topics added, still due on 29/5***
(4 June)Homework
4, due in the last class on 26/6. Updated HW4 with problem on boosting added.
(11 June)Code for running boosting and support vector regression on competition data.
(13 June)Updated Homework
4, with problem on boosting added, due in the last class on 26/6.
(19 June)Blog post applying deep learning to image classification by former class student Giora Simchoni.
(22 June)Competition final results: Congratulations to group "The OOBs": Dana Kaner, Aviv Navon, Dor Bank, our winners at 0.7591. We will hear from them on Monday about how they got there.
Group "PowerPuffs" gets an honorable mention for being the only other group below 0.76.
Overall, we had 28 teams, of them 21 reached the bonus.
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:
Introduction (text chap. 1,2): Local vs. global modeling; Overview of statistical considerations: Curse of dimensionality, bias-variance tradeoff; Selection of loss functions; Basis expansions and kernels
Linear methods for regression and their extensions (text chap. 3): Regularization, shrinkage and principal components regression; Quantile regression
Linear methods for classification (text chap. 4): Linear discriminant analysis; Logistic regression; Linear support vector machines (SVM)
Classification and regression trees (text chap. 9.2)
Model assessment and selection (text chap. 7): Bias-variance decomposition; In-sample error estimates, including C_{p} and BIC; Cross validation; Bootstrap methods
Basis expansions, regularization and kernel methods (text chap. 5,6): Splines and polynomials; Reproducing kernel Hilbert spaces and non-linear SVM
Committee methods in embedded spaces (material from chaps 8-10): Bagging and boosting
Deep learning and its relation to statistical learning
Case studies: Customer wallet estimation; Netflix prize competition; maybe others...
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
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
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.
File translated from
T_{E}X
by
T_{T}H,
version 4.08. On 22 Jun 2017, 12:19.