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About
CS 189 is the Machine Learning course at UC Berkeley. In this guide we have created a com-
prehensive course guide in order to share our knowledge with students and the general public,
and hopefully draw the interest of students from other universities to Berkeley’s Machine Learning
curriculum.
This guide was started by CS 189 TAs Soroush Nasiriany and Garrett Thomas in Fall 2017, with
the assistance of William Wang and Alex Yang.
We owe gratitude to Professors Anant Sahai, Stella Yu, and Jennifer Listgarten, as this book is
heavily inspired from their lectures. In addition, we are indebted to Professor Jonathan Shewchuk
for his machine learning notes, from which we drew inspiration.
The latest version of this document can be found either at http://www.eecs189.org/ or http:
//snasiriany.me/cs189/. Please report any mistakes to the staff, and contact the authors if you
wish to redistribute this document.
Notation
Notation Meaning
R set of real numbers
R
n
set (vector space) of n-tuples of real numbers, endowed with the usual inner product
R
m×n
set (vector space) of m-by-n matrices
δ
ij
Kronecker delta, i.e. δ
ij
= 1 if i = j, 0 otherwise
∇f(x) gradient of the function f at x
∇
2
f(x) Hessian of the function f at x
p(X) distribution of random variable X
p(x) probability density/mass function evaluated at x
E[X] expected value of random variable X
Var(X) variance of random variable X
Cov(X, Y ) covariance of random variables X and Y
Other notes:
• Vectors and matrices are in bold (e.g. x, A). This is true for vectors in R
n
as well as for
vectors in general vector spaces. We generally use Greek letters for scalars and capital Roman
letters for matrices and random variables.
• We assume that vectors are column vectors, i.e. that a vector in R
n
can be interpreted as an
n-by-1 matrix. As such, taking the transpose of a vector is well-defined (and produces a row
vector, which is a 1-by-n matrix).
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