Week#

Date

Topic

Further Reading

Discussion Session

Homework

Course Project

1

Jan. 7

Introduction [slides] and Math Review

• Book Chapter 1, 2, 3
• Review of probability from a course by David Blei from Princeton U.
• Machine Learning Math Essentials by Jeff Howbert from Washington U.
• http://cs229.stanford.edu/section/cs229-prob.pdf
• optimization

1

Jan. 9

Linear Regression [slides];

• http://cs229.stanford.edu/notes/cs229-notes1.pdf

• Machine Learning Math Essentials by Jeff Howbert from Washington U.
• Week 1 Slides

2

Jan. 14

Logistic Regression [slides];

Course Project Introduction [slides]

• http://cs229.stanford.edu/notes/cs229-notes1.pdf

HW1 out

2

Jan. 16

Decision Tree; Regression Tree; Random Forest [slides]

• Decision Tree: Book Chapter 8.1, 8.2
• Regression Tree: http://www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf
• Random Forest: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

• Week 2 Slides

Group formation due

3

Jan. 21

Martin Luther King, Jr, holiday (no class)

3

Jan. 23

SVM [slides]

• Notes on SVM by Andrew Ng: http://cs229.stanford.edu/notes/cs229-notes3.pdf
• SMO: http://cs229.stanford.edu/materials/smo.pdf

• Week 3 Slides

HW1 due

4

Jan. 28

Neural Network [slides]

• http://neuralnetworksanddeeplearning.com/
• http://www.deeplearningbook.org/

HW2 out

4

Jan. 30

Similarity measure and KNN [slides]

• http://scott.fortmann-roe.com/docs/BiasVariance.html

• Week 4 Slides

5

Feb. 4

Classification Evaluation; Other Practical Issues [slides]

• Book Chapter 8.5

HW2 due

5

Feb. 6

Clustering Basics: K-means; Hierarchical Clustering; DBSCAN [slides]

• Book Chapter 10.1-10.4

• Week 5 Slides

HW3 out

6

Feb. 11

Mixture Models [slides]

• http://www.stat.cmu.edu/~cshalizi/350/lectures/29/lecture-29.pdf
• http://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall06/reading/mixtureModels.pdf

6

Feb. 13

Clustering Evaluation; Other Practical Issues [slides]

• Book Chapter 10.6, 2.4

• Week 6 Slides

HW3 due

HW4 out

7

Feb. 18

Presidents’ Day holiday (no class)

Midterm Report due

7

Feb. 20

Midterm Exam (in-class)

• Midterm Paper Review

8

Feb. 25

Frequent Pattern Mining and Association Rules I [slides]

• Book Chapter 6

HW4 due

8

Feb. 28

Frequent Pattern Mining and Association Rules II

• Book Chapter 6

• Week 8 Slides

 HW5 out

9

Mar. 4

Sequential Pattern Mining [slides]; DTW [slides]

• chapter_8.pdf

9

Mar. 6

Naive Bayes for Text [slides]

• http://www.ccs.neu.edu/home/yzsun/classes/2014Fall_CS6220/Slides/NB.pdf

• Week 9 Slides

HW5 due

HW6 out (Optional)

10

Mar. 11

Topic Model [slides]

• pLSA tutorial: http://arxiv.org/pdf/1212.3900.pdf
• topic modeling tutorial: https://www.cs.princeton.edu/~blei/kdd-tutorial.pdf

10

Mar. 13

Final review [slides]

• Go over course material

 HW6 due (Optional)

11

Mar. 19

Final Exam (8:00am - 11:00am)

Final Report due (Mar. 20th)