girafe-ai / math-basics-for-ai

Math basics course materials

Logistics

• Lecturer: Evgeniya Korneva
• Pre-recorder video lectures: see group chat.
• Live practical sessions: Mondays & Fridays 19:00 Moscow time. Recordings are uploaded afterwards.
• Office hours: upon request

Useful Resources

Linear Algebra

• (course) Topics in Linear Algebra: lecture notes + quizes.
• (Youtube playlist) Linear Algebra for Engineers: a series of videos covering the most important concepts.
• (lecture notes) Linear Algebra in 25 Lectures (UC Davis)
• (book) Introduction to Applied Linear Algebra
• (book) Deep Learning - Part I

Calculus

• (Youtube playlist) Essence of Calculus
• (lecture notes) Introduction to Differential Calculus [pdf]
• (lecture notes) First Semester Calculus [pdf]

General

• (book) Mathematics for Machine Learning

LaTeX

• Learn LaTeX in 30 minutes – an Overleaf guide
• A series of great YouTube tutorials:
  • part 1: intro and overview of the very basics;
  • part 2: tables, figures, theorems and more;
  • part 3: writing a thesis with LaTeX.
• Detexify - draw a symbol you are looking for, and this web will give you its latex representation.

Graded assignments

• Final exam [pdf][LaTeX template][submission form]
  • Deadline: Monday, November 27, 18:59 Moscow time
• Graded assignmnet 4 [pdf][LaTeX template][submission form]
  • Deadline: Monday, November 6, 18:59 Moscow time
• Graded assignmnet 3 [pdf][notebook (task 2)][LaTeX template][submission form]
  • Deadline: Monday, October 23, 18:59 Moscow time
• Graded assignment 2 [notebook] [submission form]
  • Deadline: Monday, October 16, 18:59 Moscow time
• Graded assignment 1 [pdf] [LaTex template] [submission form]
  • Deadline: Monday, October 9, 18:59 Moscow time

Agenda

0. Monday, Sept 25: Introduction

• Welcome quiz [google form]
• Homework (not mandatory) - getting familiar with LaTeX
  • create an Overleaf account;
  • check out some of the tutorials (e.g., mentioned above);
  • practice: recreate the formulas you see (try not to look at the source first!) [link].

1. Friday, Sept 29

• Color vectors [notebook][solutions]
• Linear classifier [notebook][solutions]
• Homework (not graded):
  • Word vectors [notebook][solutions]
  • lecture 1 quiz [google form]
  • watch lecture 2

2. Monday, Oct 2

• Quiz review: norms
• Cosine similarity vs. Euclidian distance
• Gram-Schmidt process
• Homework:
  • Gram-Schmidt process [notebook][solutions]
  • watch lecture 3
  • graded assignment 1 is OUT (deadline next Monday before the class)

3. Friday, Oct 6

• Quiz: lectures 1 - 3 [google form]
• Method of least squares
• Homework
  • (extended to graded assignmnet) Python practice [notebook]
  • watch lecture 4
  • graded assignment 1 (deadline Monday, October 9 before the class)

4. Monday, Oct 9

• Quiz: [google form]
• Homework:
  • graded assignment 2 (deadline Monday, October 16 before the class)
  • review eigendecomposition

5. Friday, Oct 13

• PCA
  • Python practice [notebook][solutions]

6. Monday, Oct 16

• SVD
  • Python practice [notebook][solutions]

7. Friday, Oct 20

• Quiz: [google form]

8. Monday, Oct 23

• Multivariate functions

9. Friday, Oct 27

• Chain rule
• Matrix derivatives

10. Monday, Oct 30

• Gradient descent
• Integration techniques