This course is mathematically oriented, and undergraduate-level
knowledge of probability and linear algebra is a prerequisite. If you
need to brush up, here’s what you should review:
- Review of
Probability Theory
(http://cs229.stanford.edu/section/cs229-prob.pdf)
- In Boyd and
Vandenberghe “Introduction to Applied Linear Algebra”
(http://vmls-book.stanford.edu/vmls.pdf):
- Section I, Chapter 1 (Vectors): vectors, vector addition,
scalar-vector multiplication, inner product (dot product), complexity of
vector computations
- Section I, Chapter 3 (Norm and distance): Norm of a vector,
euclidean distance, complexity
- Section II, Chapter 5 (Matrices): matrix notation, zero and identity
matrices, sparse matrices, matrix transposition, matrix addition,
scalar-matrix multiplication, matrix norm, matrix-vector multiplication,
complexity
- Section II, Chapter 8 (Linear equations): systems of linear
equations
- Section II, Chapter 10 (Matrix multiplication): matrix-matrix
multiplication
- Section II, Chapter 11 (Matrix inverses): Inverse, solving a system
of linear equations
- Also a quick optimization review: Appendix C (Derivatives and
optimization)
- And a brief introduction to algorithm complexity: Appendix B
(Complexity)