General Information
Objective: Study of the main mathematical tools for machine and deep learning methods.
Syllabus: Linear Algebra, Vector Calculus, Probability and Distributions, Optimization, Implementation of Classical ML models.
Duration/credits: 60 hours/4 credits (16 weeks).
Time: Thursdays (15:50 - 19:30).
Grade: Assignments (60%) and Test (40%).
Lecturer: André Eugenio Lazzaretti.
Bibliography and Supporting Materials
Book:
Deisenroth, M. P.; Faisal, A. A. & Ong, C. S. (2020), Mathematics for Machine Learning, Cambridge University Press.
Solution guide (link).
Week 4 - 10/09
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 5 - 17/09
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 6 - 24/09
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 7 - 01/10
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 8 - 08/10
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 9 - 15/10
Test 1
Week 10 - 22/10 (ASYNC)
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 11 - 29/10 (ASYNC)
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 12 - 05/11
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 13 - 12/11
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 14 - 19/11
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 15 - 26/11
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 16 - 03/12
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 17 - 10/12
Content:
Slides (link).
Assignments:
Assig. 1 (link)
Assig. 2 (link)
Assig. 3 (link)
Week 18 - 17/12
Test 2