**Linear Algebra for Data Science & Artificial Intelligence **(1914101)

**Course syllabus** (PDF)

**Lecture notes***

**Week 1:** Introduction to Linear Algebra

**Week 2:** Vectors

**Week 3:** linear_functions

**Week 4:** Norm_&_distance

**Week 5:** Clustering

**Week 6:** Linear independence

**Week 7:** Matrices – zero & identity matrices, transpose, addition, and norm, matrix- vector multiplication

**Week 8:** Matrices – geometric transformations, selectors, incidence matrix and convolution

**Week 9:** Matrices – linear and affine functions

**Week 10:** Matrices – multiplication, linear functions composition, QR factorization

**Week 11:** Matrices – inverse matrices, eigenvalues and eigenvectors

**Week 12:** Least squares – least square problem

**Week 13:** Least squares – least data fitting

**Week 14:** Least squares – validation and feature engineering

**Week 15:** Least squares – classification

Semesters’ overall marks can be checked here. (NEW)

**Textbook:** Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe (PDF)

**Coding book:** Python Language Companion to Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares (PDF)

** Programming environment:** Anaconda Python distribution (version 3)