Statistical Learning

This course covers the machine learning framework from a statistical perspective.
We will focus primarily on supervised learning (regression and classification) and introduce some elements of unsupervised learning through clustering methods.
Beyond modeling and theory, the course will also cover some aspects of optimization and implementation (sklearn, PyTorch, etc.) of the methods introduced.

Be able to model a new learning problem based on the objectives and available methods.

Linear Modeling (HAX814X) / Software Development (HAX712X) / Optimization (HAX706X)

 
 
Recommended prerequisites: Inferential Statistics (HAX710X) and Nonparametric Estimation and Tests (HAX809X)

CC

Graded practical assignment (code)

Project (report + presentation + code)

• Introduction to Supervised Learning; Linear Models.
• Cross-validation, logistic regression, discriminant analysis.
• Model Selection and Regularization Methods.
• Performance metrics (multi-class: top-k, ROC curve AUC, etc.)
• Perceptron and stochastic gradient descent.
• SVM
• Decision Trees, Random Forests, and Boosting.
• Unsupervised learning (clustering: K-means, Ward's method)
• Neural Networks 

 
Hours:
Lectures: 9:00 p.m.
Tutorials:
Lab:
Fieldwork: