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.