Undergraduate Courses

Department of Mathematical and Industrial Engineering offers four versions of Probability and Statistics course for engineers.
MTH 2302A: for Physical and Electrical Engineers.
MTH 2302B: for Chemical, Mechanical, and Material Engineers.
MTH 2302C: for Geology, Civil, and Mine Engineers.
MTH 2302D: for Industrial, Computer, and Software Engineers.
I regularly teach MTH 2302D.

Graduate Course

I mounted a Data Mining course MTH 6312 after my arraival to Ecole Polytechnique.

MTH 6312 is a graduate course targeting Statistical Learning and Data Mining from methodological point of view. The official page of the course on the Polytechniques graduate course web list is here. If you had problems with taking the course, contact our graduate office at Ecole Polytechnique or send an email to vahid d0t partovinia at-sign polymtl d0t ca. The course will be given every fall. Lectures will appear at one of the Ecole Polytechnique de Montreal's buldings. The teaching language is flexible (English or French) depending on participants.

The main reference books of the course are

An Introduction to Statistical Learning

The book website is here. Download the book all in one PDF from here.

The Elements of Statistical Learning

The book website is here. Download the book all in one PDF from here.

Machine Learning: A Probabilistic Perspective

The book website is here. If you need more support check here.

We discuss the new supervised, semi-supervised and unsupervised methods. Main subjects include:
Loss Function and Optimization
Bias-variance trade-off, model selection criteria
Bayesian linear models
Lasso regression, Least angle regression
Nonparametric regression and smoothing splines
Discrimination techniques (logistic regression, linear and quadratic discriminants, nonparametric logistic regression, mixture of discriminants)
Separating hyperplanes and support vector machines
Additive trees, MARS, and CART
EM algorithm and applications in semi-supervised learning
Bagging and boosting
High-dimensional situations
Bayesian parametric clustering
Bayesian functional clustering
Bayesian nonparametric models and Dirichlet processes
Variational inference.

To get an idea what this course is about, draw in the square and a k-nearest-neighbour classification algorithm recognizes the digit you drew.
Check a nice convolutional neural networks which is much more precise compred to nearest-neighbours HERE.
Check another visualization on deep neural networks by Google HERE.

Chicago Crime Density

If you want to buy a house in Chicago, better to look at the crime density. Crime data are available publicly by the City of Chicago

Mitterand versus Chirac

Do you look for more fun with machine learning? My research team figures out which one is more likely to talk like you Mitterand or Chirac?