Linear Algebra with Applications in Machine Learning and Data Science
All resources (including lecture slides, homework, starter files, articles) can be found under the Resources tab.
The topics to cover and the readings to be assigned are subject to change.
| Date | Topics | Homework |
|---|---|---|
| Feb 19 Feb 26 |
Lecture 1: Linear Combinations and ML Applications and applications to ML Lecture 2: The Dot Product and its Geometry and applications to ML |
|
| Mar 5 Mar 12 |
Lecture 3: Lines Planes and the Cross Product and applications to ML Lecture 4: Systems of Linear Equations and applications to ML |
Due:
|
| Mar 19 Mar 26 |
Lecture 5: Row Echelon Forms and applications to ML Lecture 6: Span and Linear Dependence and applications to ML |
Due:
|
| Apr 2 Feb 9 |
Lecture 7: Matrix Operations and applications to ML Lecture 8: Invertability and applications to ML |
Due: |
| Apr 16 Apr 23 |
Lecture 9: Subspaces and applications to ML Lecture 10: Bases and applications to ML |
Due: |
| Apr 30 May 7 |
Lecture 11: Invertible Matrix Theorem and applications to ML Lecture 12: Linear Transformations and applications to ML |
Due: |
| May 14 May 21 |
Lecture 13: Determinants and applications to ML Lecture 14: Determinant Geometry and applications to ML |
|
| May 28 Jun 4 |
Lecture 15: Matrices and Eigen"stuff" and applications to ML Lecture 16: Eigenvectors and Eigenvalues and applications to ML |
Due: |