Math for Artificial Intelligence and Data Science

This course provides basic tools from linear algebra, calculus, optimization, and probability necessary for understanding algorithms in Artificial intelligence and Data Science.

Recommended Literature:

• DAVID C. LAY, STEVEN R. LAY, JUDI J. MCDONALD. Linear Algebra and Its Applications, Pearson; 5th edition 2014, ISBN: 978-0321982384.
• MARC PETER DEISENROTH, A. ALDO FAISAL, CHENG SOON ONG. Mathematics for Machine Learning, Cambridge University Press; 1st edition 2020, ISBN 978-1108470049.
• STEPHEN BOYD, LIEVEN VANDENBERGHE. Convex Optimization, Cambridge University Press, 1st edition 2004, ISBN: 978-0521833783.