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)