Week#

Date

Topic

Further Reading

Discussion Session

Homework

Course Project

1

Oct. 2

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

Oct. 4

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

Oct. 9

Logistic Regression [slides];

Course Project Introduction [slides]

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

2

Oct. 11

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

HW1 out

Group formation due

3

Oct. 16

SVM [slides]

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

3

Oct. 18

Neural Network [slides]

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

• Week 3 Slides

HW1 due

HW2 out

4

Oct. 23

Similarity measure and KNN [slides]

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

4

Oct. 25

Classification Evaluation; Other Practical Issues [slides]

• Book Chapter 8.5

• Week 4 Slides

5

Oct. 30

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

• Book Chapter 10.1-10.4

HW2 due

HW3 out

5

Nov. 1

Density Estimation; [slides]

• https://www.homeworkhelponline.net/blog/math/tutorial-kde
• http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2015/tutorials/r3_kde.html
• http://users.monash.edu/~webb/Files/LiuYangWebbBoughton09.pdf

• Week 5 Slides

6

Nov. 6

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

Midterm Report due

6

Nov. 8

Clustering Evaluation; Other Practical Issues [slides]

• Book Chapter 10.6, 2.4

• Midterm Review

 HW3 due

7

Nov. 13

Midterm Exam (in-class)

7

Nov. 15

Frequent Pattern Mining and Association Rules I [slides]

• Book Chapter 6

• Midterm Paper Review

8

Nov. 20

Frequent Pattern Mining and Association Rules II

• Book Chapter 6

 HW4 out

8

Nov. 22

Sequential Pattern Mining [slides]

• http://web.engr.illinois.edu/~hanj/cs512/bk2chaps/chapter_8.pdf

• Thanksgiving Holiday

9

Nov. 27

Sequential Similarity Search [slides]

9

Nov. 29

Naive Bayes for Text [slides]

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

• Week 9 Slides

 HW4 due

10

Dec. 4

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

Dec. 6

Final review [slides]

• Go over course material

Final Report due

11

Dec. 13

Final Exam