CS6220 / DS 5230 schedule

CS6220/DS5230 Unsupervised Data Mining, Summer 2024

About CS6220 Home Schedule Piazza Gradescope DM Resources

* Schedule and materials subject to change

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Module / Live Stream	Topic / Recorded.Lecture	Other Reading	Assignment
5/8 Module 1 : Data Basics, Similarity, KNN Week 1 : Intro, Data Features, Mining Rules	Slides: DM Intro Slides: ML Intro Slides: Frequent Sets Mining Slides: Frequent Sets: Apriori Algorithm	Background: Probabilities Linear algebra Programming: MATLAB, Java, Python, R	HW 1 Due: 5/21
5/15 Week 2 : Similarity, KNN Apriori Algorithm Lecture 3 Notes	Slides: Distance and Similarity Paper: Distance / Similarity Measures	[A] ch 3
5/22 Module 2: Clustering Week 3 : KMeans Lecture 4 Notes	Slides: Intro to Clustering Cluster Evaluation (Aggarwal) Cluster Evaluation (Stanford NLP)		HW 2A Due: 5/31
5/29 Week 4 : soft KMeans / Gaussian Mixture EM Lecture 5 Notes Lecture 6 Notes	Notes: Gaussian Mixtures Mixture Matlab code		HW 2B Due: 6/11
6/5 Week 5 : Hierarchical, DBScan Lecture 7 Notes Ward distance
6/12 Module 3: Dim Reduction, Feature Selection Week 6 : PCA, feature Selection Lecture 8 (PCA, kernelPCA)	Notes: PCA Class notes (handwritten+ DHS book): PCA PCA demo (Matlab): PCA	Kernel PCA (slides) Kernels for ML (article) optional: UMAP Dimension Reduction , UMAP-paper	HW3A Due: 6/21
6/19 Week 7 : tSNE, Feature Selection Lecture 9 Notes	Paper: Harr Features Notes:ChiSquare_FeatureSelection Wikipedia: Mutual Information Slides: tSNE / paper / implementation (optional) tSNE gradient calculation	StanfordNLP: ChiSquare Feature Selection StanfordNLP: Mutual Information Feature Selection Paper: Feature Section for Gaussian Mixtures
6/26 Week 8 : Supervised Classification Lecture 10 Notes Linear Regression	Notes: Linear Regression Notes: Logistic Regression Notes: Regression Regularization		HW3B Due: 7/3
7/10 Module 3: Classification Week 8 : Supervised Classification Neural Networks Decision Trees Lecture 12 Notes Decision Notes (Virgil) Boosting Notes	Notes: Decision Trees Notes: Perceptrons, Neural Networks Slides (Mitchell book): Neural Networks TF Visualizer (toy data) NN interactive tutorial Word2Vec Tutorial	Word2Vec paper 1 Word2Vec paper 2	HW4 HW4-PB2, due 7/12
7/17 Week 11 : Summarization Lecture 15 NMF Lecture 16 Summarization	Paper: Text Summarization Survey Paper: Topic Modeling Summarization Paper: ROUGE Evaluation for Summaries Slides: ROUGE	IR/Linguistics old paper: Automatic Abstracts Summarization basics	HW5A Due: 7/23
7/24 Module 4: Text Modeling Week 9 : Topic Models, LDA Lecture 13 Notes Lecture 14 Notes Lecture 15 NMF	Slides: NMF paper: NMF Slides: LDA paper: LDA simplified	paper: LDA More Slides: LDA paper: Bayesian Parameter Estimation for text Book on Implementing LDA with R code paper: LDA vs NMF	HW5B Due: 8/6
7/31 Week 10 : Sampling Lecture 17 Markov chains Lecture 18: Sampling Stevens Method: Sample Non-uniform Without Repetition	Sampling Basics (Matlab) Rejection Sampling Inverse Transform Sampling Book: Un-uniform Sampling Procedures Gibbs Sampling for LDA Sampling MC/ Gibbs Demo	paper: Gibbs explained
8/7 Module 5: Graphs/ Social Mining Week 12 : Social Graphs Lecture on PageRank, Markov Chain Lecture 19 Graph Intro/Communities Lecture 20 Graph Communities	Textbook: Aggarwal, Data Mining, ch 18-19 Slides: Girvan - Newman Algorithm Python Community Visualization	Paper1: Girvan - Newman Algorithm Paper2: Girvan - Newman Algorithm Paper3: Girvan - Newman Algorithm	HW6 Due: 8/13
8/7 Week 13 : Social Mining Lecture 18 Collab Filtering Lecture 21 KB-QA	Textbook: Aggarwal, Data Mining, ch 18-19 Notes: collaborative fiiltering basic formula Slides: Netflix User Profiles
	FINAL EXAM 8/14 2pm-6pm in class You will need a computer for the exam problems, and might be called to explain/demo your code after. Submit a copy of your code on gradescope together with running instructions.