[9/28/2015] Office hours have been changed to Tuesday afternoons 3:30-5:30pm
[9/14/2015] First day of classes
(Future lectures and events are tentative.)
| Week# | Date | Topic | Slides | Assignment | Project | Reading (Textbook or Other Materials) |
| 2 | Sep. 14 | Introduction and Know Your Data |
01Introduction 02Data |
Chapter 1, 2, 3 Math overview:
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| 3 | Sep.21 |
Course Project Introduction Matrix Data: Prediction (linear regression); Classification (decision tree, evaluation) |
Course Project Overview 03Matrix_Prediction 04Matrix_Classification_1 |
#1 out | Notes by Andrew Ng (Sec. 1-3 in Part 1):
http://cs229.stanford.edu/notes/cs229-notes1.pdf Chapter 8.1, 8.2, 8.5 |
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| 4 | Sep. 28 | Matrix Data: Classification (Naive Bayes, logistic regression) |
Prob_review 04Matrix_Classification_2 |
Team formation due (Sep. 27) | Chapter 8.3, 9.1 Notes by Tom Mitchell: http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf Notes on derivation of P(C_j) in Naive Bayes review of probability: http://cs229.stanford.edu/section/cs229-prob.pdf |
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| 5 | Oct. 5 | Matrix Data: Classification (SVM, kNN, and other issues) | 04Matrix_Classification_3 | #1 due (Oct. 4)/ #2 out | Chapter 9.3, 9.5, 8.6, 9.7 Notes on SVM by Andrew Ng: http://cs229.stanford.edu/notes/cs229-notes3.pdf |
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| 6 | Oct.12 | Columbus Day (No Class) | Proposal due (Oct. 12) | |||
| 7 | Oct. 19 | Matrix Data: Clustering (k-means, hierarchical clustering, DBSCAN) | 04Matrix_Clustering_1 | #2 due (Oct. 18) / #3 out | Chapter 10.1, 10.2, 10.3, 10.4, 10.6 | |
| 8 | Oct. 26 |
Matrix Data: Clustering (GMM) Text Data: Topic Models (PLSA ) |
04Matrix_Clustering_2 05Text |
Chapter 11.1, 11.3 Notes on mixture models and EM algorithm: http://www.stat.cmu.edu/~cshalizi/350/lectures/29/lecture-29.pdf and http://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall06/reading/mixtureModels.pdf pLSA tutorial: http://arxiv.org/pdf/1212.3900.pdf topic modeling tutorial: https://www.cs.princeton.edu/~blei/kdd-tutorial.pdf |
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| 9 | Nov.2 | Set Data: Frequent Pattern Mining (Apriori, FP-growth) | 06Set | #3 due (Nov. 1) / #4 out | Chapter 6 | |
| 10 | Nov. 9 | Midterm Exam | ||||
| 11 | Nov. 16 |
Graph / Network: PageRank, Personalized PageRank Image Data: Neural Networks |
08Graph 09Image_NN |
#4 due (Nov. 15)/ #5 out | Chapter 9.2 ANN by Tom Mitchell: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/mlbook/ch4.pdf |
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| 12 | Nov. 23 | Sequence Data: Sequential pattern mining (GSP), HMM | 07Sequence | Midterm Report due (Nov. 22) | Reference:
Chapter 8.3 in Han's Data Mining Book, Edition 2 Papers: GSP, PrefixSpan |
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| 13 | Nov. 30 | Time Series: forcasting, similarity search (DTW) | 10TimeSeries | #5 due (Nov. 29) | References: DTW | |
| 14 | Dec. 7 | No Class | ||||
| 15 | Dec. 14 | Course Project Final Presentation | Final Report & Code (Dec. 14) |