Dominique Guillot (EWG 534)
University of Delaware
Spring 2016
MWF 11:15AM – 12:05PM, Room: ALS 226 (Alison Hall)
The midterm will be: March 25th 2016 (in class).
Detailed syllabus.
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, Springer, 2013.
You can download the book in pdf for free here.
We will use Python during the course. A good Python tutorial is available at http://www.scipy-lectures.org/.
The following may be useful to Matlab users: http://mathesaurus.sourceforge.net/matlab-numpy.html
If you have never used Python before, I recommend using Anaconda Python 3.5 https://www.continuum.io/. It contains all the packages we will need.
Here is a Python file containing useful basic commands to get you started. Try running the commands one by one to familiarize yourself with Python. For those who haven't used Python before, you can run the commands in “idle” (comes with Anaconda).
| Topic | Reading | Slides | Printable slides | |||
| Week 1 | 02/08/2016 | Lecture 1 | Introduction | ESL, Chapter 1 | ||
| 02/10/2016 | Lecture 2 | Review of Linear regression | ||||
| 02/12/2016 | Lecture 3 | Gauss-Markov Theorem | ESL, Chapter 3 (up to 3.2.3) | |||
| Week 2 | 02/15/2016 | Lecture 4 | Regression consistency | |||
| 02/17/2016 | Lecture 5 | Statistical tests | ||||
| 02/19/2016 | Lecture 6 | Subset selection | ESL, Chapter 3.3 | |||
| Week 3 | 02/22/2016 | Lecture 7 | Penalizing coefficients | ESL, 3.4.1, 3.4.2, 3.4.3 | ||
| 02/24/2016 | Lecture 8 | Model selection | ESL, 7.10.1, 7.10.2, 7.10.3 | |||
| 02/16/2016 | Lecture 9 | Lasso solution | ESL, 3.8.6 | |||
| Week 4 | 02/29/2016 | Lecture 10 | Least angle regression | ESL 3.4.4 | ||
| 03/02/2016 | Lecture 11 | Categorical data | ESL, 2.3 | |||
| 03/04/2016 | Lecture 12 | Intro. to statistical decision theory | ESL, 2.4 | |||
| Week 5 | 03/07/2016 | Lecture 13 | Logistic regression | ESL, 4.4 | ||
| 03/09/2016 | Lecture 14 | Linear discriminant analysis | ESL, 4.3 | |||
| 03/11/2016 | Lecture 15 | Support vector machines | ESL, 12.1, 12.2 | |||
| Week 6 | 03/14/2016 | Lecture 16 | Kernels in SVM | ESL, 12.3.1 | ||
| 03/16/2016 | Lecture 17 | Splines | ESL, 5.1-5.4 | |||
| 03/18/2016 | Lecture 18 | Lab | ESL, 5.2.3 | |||
| Week 7 | 03/21/2016 | Lecture 19 | Kernel smoothing | ESL, 6.1-6.5 | ||
| 03/23/2016 | Lecture 20 | Density estimation | ESL, 6.6 | |||
| 03/25/2016 | Midterm | In class | ||||
| Week 8 | Spring break | |||||
| Week 9 | 04/04/2016 | Lecture 21 | PCA | ESL, 14.5.1 | ||
| 04/06/2016 | Lecture 22 | Decision trees | ESL, 9.2 | |||
| 04/08/2016 | Lecture 23 | Random forests | ESL, 8.7, 15.1-15.3 | |||
| Week 10 | 04/11/2016 | Lecture 24 | Neural Networks I | ESL, 11.1-11.4 | ||
| 04/13/2016 | Lecture 25 | Neural Networks II | ||||
| 04/15/2016 | Lecture 26 | Lab II | ||||
| Week 11 | 04/18/2016 | Lecture 27 | The EM algorithm | ESL, 8.5.1, 8.5.2 | ||
| 04/20/2016 | Lecture 28 | The EM algorithm - part 2 | ||||
| 04/22/2016 | Lecture 29 | ICA | ESL, 14.7 | |||
| Week 12 | 04/25/2016 | Lecture 30 | Clustering I | ESL, 14.3 | ||
| 04/27/2016 | Lecture 31 | Clustering II | von Luxburg 2007 | |||
| 04/29/2016 | Lecture 32 | Clustering III | von Luxburg 2007 | |||
| Week 13 | 05/02/2016 | Lecture 33 | Graphical Models I | ESL, 17.1-17.2 | ||
| 05/04/2016 | Lecture 34 | Graphical Models II | ||||
| 05/06/2016 | Lecture 35 | Graphical Models III | ESL, 17.3 | |||
| Week 14 | 05/09/2016 | Lecture 36 | Markov Chains | |||
| 05/11/2016 | Lecture 37 | Hidden Markov models | ||||
| 05/13/2016 | Lecture 38 | Intro. to Bayesian analysis | ||||
| 05/23/2016 | Project presentations | 10 AM - 4 PM (Gore 114) | Schedule and abstracts | |||