Tutorial: Math for Deep Learning

The math background knowledge of students attending our lectures on deep learning, computer vision and self-driving cars is diverse. In response, we created a series of tutorials (3.5 hours in total) on relevant concepts (linear algebra, differential calculus, probability theory, information theory), primarily based on the Goodfellow and Bishop books. These micro lectures are offered to students who like to recap relevant or unknown course prerequisites through self-study. They also introduce the relevant notation that we use in our lectures.

Schedule

	Lecture Videos (Slides)
01	Introduction \| Video
02	Sets, Scalars, Vectors, Matrices and Tensors \| Video
03	Adding and Multiplying Matrices and Vectors \| Video
04	Identity and Inverse Matrices \| Video
05	Linear Dependence and Span \| Video
06	Vector and Matrix Norms \| Video
07	Special Matrices and Vectors \| Video
08	Eigenvalue and Singular Value Decomposition \| Video
09	The Trace Operator and Determinant \| Video
10	Differential Calculus \| Video
11	Vector Calculus \| Video
12	Random Variables and Probability Distributions \| Video
13	Common Probability Distributions \| Video
14	Bayesian Decision Theory \| Video
15	Expectation, Variance and Covariance \| Video
16	Information and Entropy \| Video
17	Kullback-Leibler Divergence \| Video
18	The Argmin and Argmax Operators \| Video