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Data mining aims at finding useful patterns in large data sets. This course will discuss data mining algorithms for analyzing large amounts of data, including association rules mining, finding similar items, clustering, data stream mining, recommender systems, how search engines rank pages, and recent techniques for large scale machine learning. The goal of this class is for students to understand basic and scale data mining algorithms.
- A solid programming skill (Python is preferred)
- Basic probability and statistics
- Basic linear algebra
- [MMDS] Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets. Cambridge university press. Available Online.
- [MML] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. (2020). Mathematics for Machine Learning. Available Online.
|Jan 19||Introduction to Data Mining||MMDS CH.1|
|Jan 21||Canceled||MMDS CH.2|
|Jan 26||Map-Reduce||MMDS CH.2|
|Jan 28||Matrix Multiplication by MapReduce (Optional)||MMDS CH.2|
|Feb 2||Spark||MMDS CH.2|
|Feb 4||Frequent Itemset Mining||MMDS CH.6.1-6.3; CH.6.4 (Optional)|
|Feb 9||Locality-Sensitive Hashing||MMDS CH.3.1-3.4|
|Feb 11||Locality-Sensitive Hashing||MMDS CH.3.5-3.6 (Optional)|
|Feb 16||Clustering||MMDS CH.7.1-7.3|
|Feb 18||Hierarchical clustering and K-means||MMDS CH.7.1-7.3|
|Feb 23||BRF and CURE||MMDS CH.7.3-7.4|
|Feb 25||EM algorithm (Optional)||MML CH.11.1-11.3|
|Mar 2||Gaussian Mixture Models (Optional)||MML CH.11.1-11.3|
|Mar 4||k-nn and Naive Bayes||MMDS CH.12.1,12.4|
|Mar 9||k-nn and Naive Bayes||MMDS CH.12.1,12.4|
|Mar 11||Decision Tree||MMDS CH.12.5|
|Mar 16||SVM||MML CH.12.1,12.2; MMDS CH.12.3|
|Mar 18||Course Project Proposal Presentation|
|Mar 23||SVM + Midterm Review|
|Mar 30||Mining Data Streams||MMDS CH4.1-4.3|
|April 1||Mining Data Streams||MMDS CH4.4-4.7|
|April 6||Mining Data Streams||MMDS CH4.4-4.7|
|April 13||PageRank||MMDS CH5.1-5.2|
|April 20||Course Project Presentation|
|April 22||Course Project Presentation|
- Homework (30%)
- Five programming assignments
- Course Project (40%)
- Students are required to participate in one Kaggle competition.
- The project will be evaluated based on the technical soundness, presentation, and final report.
Midterm Exam (30%)
- Class Attendance
- Class attendance is not mandatory but recommended.
- Data mining overview
- MapReduce and Spark
- Frequent itemset mining
- Finding similar items
- Mining data stream
- Dimensionality reduction
- Recommender systems
- Computational advertising
- Machine learning
- Anomaly detection