[1/13/2016] Classes start at 6:10pm
[1/13/2016] First day of classes
(Future lectures and events are tentative.)
| Week# | Date | Topic | Slides | Assignment | Project | Reading (Textbook or Other Materials) |
| 1 | Jan. 13 | Introduction and Know Your Data |
01Introduction 02Data |
Chapter 1, 2, 3 Math overview:
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| 2 | Jan. 20 |
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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| 3 | Jan. 27 | Matrix Data: Classification (Naive Bayes, logistic regression) | Prob_review 04Matrix_Classification_2 |
Team formation due (Jan. 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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| 4 | Feb. 3 | Matrix Data: Classification (SVM, kNN, and other issues) | 04Matrix_Classification_3 | #1 due (Feb. 2)/ #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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| 5 | Feb. 10 | Matrix Data: Clustering (k-means, hierarchical clustering, DBSCAN) | 05Matrix_Clustering_1 | Chapter 10.1, 10.2, 10.3, 10.4, 10.6 | ||
| 6 | Feb. 17 |
Matrix Data: Clustering (GMM) Text Data: Topic Models (PLSA ) |
#2 due (Feb. 16) / #3 out |
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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| 7 | Feb. 24 | Set Data: Frequent Pattern Mining (Apriori, FP-growth) | 07Set | Proposal due (Feb. 23) | Chapter 6 | |
| 8 | Mar. 2 | Midterm Exam | #3 due (Mar. 4) #4 out | |||
| 9 | Mar. 9 |
Spring Break |
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| 10 | Mar. 16 |
Graph / Network I: Ranking, Proximity, Clustering |
08Graph | #4 due (Mar. 15) | Spectral clustering: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5B0%5D.pdf | |
| 11 | Mar. 23 | Graph /Network II: Recommendation |
09Recommendation | #5 out (Mar. 23) | Midterm Report due (Mar. 22) | http://ijcai13.org/files/tutorial_slides/td3.pdf |
| 12 | Mar. 30 | Sequence Data and Time Series Data | 10Sequence_TS | Reference: Chapter 8.3 in Han's Data Mining Book, Edition 2; GSP; DTW | ||
| 13 | Apr. 6 | Image Data: Neural Networks, deep learning | #5 due (Apr. 9) | |||
| 14 | Apr. 13 | No class | ||||
| 15 | Apr. 20 | No class |
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| 16 | Apr. 27 | Course Project Final Presentation | Final Report & Code (Apr. 27) |