Numerical Linear Algebra is a fundamental tool for data science and machine learning. This course will introduce basic concepts and provide a mathematical foundation for research in machine learning and data science. We will discuss how numerical linear algebra is useful and essential in several key machine learning and data science topics.
Fundamentals (matrix, orthogonality, norms, SVD, QR factorization, Gram-Schmidt Orthogonalization, Numerical methods), Application to Machine Learning and Data science topics (Least squares problems, Principal component analysis).
- Instructor: Prof. Lily Weng, HDSI
- Akshay Kulkarni, CSE
- Yilan Chen, CSE
- Ge Yan, CSE
- Tuomas Oikarinen, CSE
For contact information, see Staff page.
For course logistics, see Logistics page.
For course timeline, slides, and scribe-notes, see Course Timeline page.