Notice: Solutions for HW 6 and 7 have been uploaded.
Notice: Review Notes and Slides have been uploaded.
In this course we will cover the basics of three topis, namely Real Analysis, Probability, and Linear Algebra.
The course is intended to be a refresher. Most Machine Learning courses expect students to have some background in these three topics. I expect that you will have taken one or two courses in each of them. I also expect that many, if not most of you have also forgotten much of what you studied. For others, one or more of these topics will be new. The aim of this course is to get you quickly up to speed in these three topics.
The course will be very fast paced. We will, in essence, be covering three semesters worth of material in one semester. We will cover each of the three topics for roughly one month. The lectures will only skim over the topics. You are expected to fill in the extra material yourself by studying the suggested textbooks. For details on the books we will use and the chapters we will cover, click on the “Books” link to the left.
At the end of each week you will be assigned a homework. Homeworks will be short — typically four or five questions. Questions may be from topics not covered in class, but covered in the textbooks.
Grades: Grading will be based on attendance (5%), homeworks (50%), midterms (20%) and finals (25%).
Instructor: Bhiksha Raj
TAs: Xiangzhu Long (xiangzhl@andrew.cmu.edu), Sai Miryala (smiryala@cs.cmu.edu)
Location: GHC 4125
Time: Mondays and Wednesdays, 4.30pm-5.50pm
Office hours: