Machine Learning
Andrew Ng
Course Description
In this course, you'll learn about some of the most widely used
and successful machine learning techniques. You'll have the opportunity
to implement these algorithms yourself, and gain practice with them. You will
also learn some of practical hands-on tricks and techniques (rarely discussed
in textbooks) that help get learning algorithms to work well. This is
an "applied" machine learning class, and we emphasize the intuitions and
know-how needed to get learning algorithms to work in practice, rather than
the mathematical derivations.
Familiarity with programming, basic linear algebra (matrices, vectors,
matrix-vector multiplication), and basic probability (random variables,
basic properties of probability) is assumed. Basic calculus (derivatives
and partial derivatives) would be helpful and would give you additional intuitions
about the algorithms, but isn't required to fully complete this course.
I. INTRODUCTION
-  Welcome
-  Installing Octave
II. LINEAR REGRESSION I
-  Exercise 2
III. LINEAR REGRESSION II
-  Exercise 3
IV. LOGISTIC REGRESSION
-  Exercise 4
V. REGULARIZATION
-  Exercise 5
VI. NAIVE BAYES
-  Exercise 6
VII.
-  Exercise 7
VIII.
-  Exercise 8
IX.
-  Exercise 9