MATHEMATICS FOR MACHINE LEARNING

Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes. Most subjects presented here are covered rather minimally; we intend to give an overview and point the interested reader to more comprehensive treatments for further details.

[1]  Gerald B. Folland,et al.  Real Analysis: Modern Techniques and Their Applications , 1984 .

[2]  J. Rice Mathematical Statistics and Data Analysis , 1988 .

[3]  S. Axler Linear Algebra Done Right , 1995, Undergraduate Texts in Mathematics.

[4]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[5]  J. Rosenthal A First Look at Rigorous Probability Theory , 2000 .

[6]  E. Cheney Analysis for Applied Mathematics , 2001 .

[7]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[8]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..