Selected Topics on Machine and Deep Learning, 2026

• This page is used for course information and materials.
• The complimentary Moodle page is used for course communication and submissions.

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Materials

• Lecture notes
• Recommended AI tools

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Meetings

Week 1 (09/03)

• Topic: Descriptive Statistics Basics (Ch. 1)
  • whiteboard
  • recording
• classwork:
  • dataset
  • report
• homework: reproduce the classwork report with the following dataset from here
  • Submission in PDF format
  • Moodle upload

Week 2 (16/03)

• Topics:
  • Linear LS Basics and multivariate LS (Ch. 2, except (*)-marked section, Ch. 3.1, 3.2.1, 3.3.3)
  • Agentic AI
• Files:
  • Ch. 2
  • Ch. 3
  • Agentic AI
• recording
• homework: install and test one of the recommended CLI AI tools

Week 3 (23/03)

• Topics:
  • Model characterization (Ch. 4, except (*)-marked sections)
  • Agentic AI (cont.)
• File Ch. 4
• recording

Week 4 (30/03)

• Topics:

  • Regression losses and metrics (Ch. 6, except (*)-marked sections)
  • Homework 2 (presented in class)
  • Agentic AI (prompt engineering and plan mode)

• Files:

  • Ch. 6
  • recording
  • dataset for homework

• Note:

  • 06/04 - no class (Passover),
  • 13/04 - no class (יום השואה),
  • 20/04 - no class (יום הזיכרון)

Week 5 (27/04)

• Topics:
  • Learning systems engineering (Ch. 7)
  • Agentic AI (memory and skills)
• Lecture notes
• recording

Week 6 (04/05)

• Topic: Classification - logistic regression (Ch. 8)
• recording
• Lecture notes

Week 7 (11/05)

• Topic:
  • Classification performance metrics - lecture notes
  • Homework 3 (presented in class)
  • recording

Week 8 (18/05)

• Topic:
  • Classifier comparison
  • recording
  • lecture notes

Week 9 (25/05)

• Topic:
  • Introduction to deep learning
  • recording
  • lecture notes

Week 10 (01/06)

• Topic:
  • Introduction to computer vision
  • recording
  • lecture notes

Week 11 (08/06)

• Topic:
  • Summary
  • Homework 4 (presented in class)

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