Machine learning course, 2026

  • This page is used for course information and materials.
  • The complementary Moodle page is used for course communication and submissions.
  • Subject to changes and updates during the course.

Lecture notes

Note, to be updated during the course

Recordings and supplementary materials

Week1 (11/03)

  • Material: Descriptive Statistics Basics and Linear LS Basics (Ch. 1 and 2, except (*)-marked sections)
  • whiteboard
  • recording

Week2 (18/03) - Multivariate LS

Week3 (25/03) - Model characterization and cross-validation

Week4 (15/04) - Overfitting, regularization, and bias-variance tradeoff

Week5 (29/04) - Classification, logistic regression, and cross-entropy loss

Week6 (06/05) - DLI: Accelerating End-to-End Data Science Workflows

Week7 (13/05) - Midterm

Week8 (20/05) - Signal analysis

Week9 (27/05) - Signal analysis (continued)

Week10 (03/06) - No class (work on the project)

Week11 (10/06) - No class (work on the project)

Project presentations (14/06) as a part of SCETech26

Previous exams