Week1 (11/03)

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

Week2 (18/03)

• Material: Multivariate LS (Ch. 3)
• notes
• AI example
• recording

Week3 (25/03)

• Material: Model characterization (Ch. 4 except (*)-marked sections, 7.1.1)
• Ch. 4 notes
• Ch. 4 example
• Ch. 7 notes
• recording

Week4 (15/04) - physical class

• Material: Logistic regression (Ch. 8) and feature selection (Ch. 9)
• recording

Week5 (29/04)

• Material: Classification performance evaluation (Ch. 11)
  • recording

Week6 (06/05)

• Material: Hands-on session with Accelerating End-to-End Data Science Workflows (computer classroom session)

Week7 (13/05)

• Midterm

Week8 (20/05)

• Project task presentation
• recording

Week9 (27/05)

No class (work on the project)

Week10 (03/06)

• Project per-group meeting (zoom, 15 minutes per group)

Week11 (10/06)

No class (work on the project)

Project presentations (14/06 10:00-14:00)

As a part of SCETech26