CS 341: Machine Learning

Week Date Topics Reading/Resources
 Assignments
1 9/5 What is Machine Learning? [slides]
 RN 18.1–18.2; Coursera Lec. I
 self assessment
2 9/10 Supervised learning; Linear regression in one variable [notes]
 RN 18.6.1; Coursera Lec. I & II
 Exercises (Ex 1. due Wed 9/12; Ex. 2 due Monday 9/17).
9/12 Gradient Descent [notes]
 Previous + R&N 4.2
 HW1 due Wed 9/19
3 9/17 Linear Algebra review (optional) [slides][annotated]
 Coursera Lec. III.
[Linear Algebra notes - Stanford CS 229, by Kolter, Do]
9/19 Multivariate linear regression; MATLAB [slides][annotated]
 RN 18.6.2; Coursera Lec. IV
MATLAB: Coursera Lec. V; MathWorks video examples
 HW2 [pdf][zip] due Wed 9/26
4 9/24 Logistic regression [slides]
 RN 18.6.2–18.6.4; Coursera Lec. VI
9/26 Logistic regression; Overfitting & Regularization
 Coursera Lec. V
 HW3 posted on ella; due Wed 10/3
5 10/1 Overfitting & Regularization [slides][annotated]
 Coursera Lec. VII
10/3 Decision Trees [slides][annotated]
 RN 18.3
6 10/8 MID SEMESTER BREAK (NO CLASS)

10/10 Decision Trees continued
HW4 posted on ella; due Wed 10/17 Fri 10/19
7 10/15 Multi-class Classification [slides]

10/17 MOUNTAIN DAY (NO CLASS)
HW 5 posted; due Wed 10/24
8 10/22 Support Vector Machines [notes]

10/24 Projects; Intro to Unsupervised Learning [slides]
 Project info
 Project proposal out; Mid-semester eval out; due 10/31
9 10/29 NO CLASS. HURRICANE SANDY

10/31 Support Vector Machines [notes]
 R&N 18.9; Coursera XII
 Project proposal due (now due Friday); HW 6 posted, due Wed. 11/7
10 11/5 Instance-Based Learning and Kernel Regression [notes]
 R&N 18.8
11/7 Kernelized SVMs [notes]

11 11/12 Methodology [slides]
 R&N 18.4
11/14 PCA

12 11/19


11/21 THANKSGIVING RECESS (NO CLASS)

13 11/26

Mid-project report due
11/28


14 12/3


12/5


15 12/10 Project presentations
Project presentations
12/11
  –––– MHC last day of classes –––––
Project report due