EE448 Big Data Mining 2019
John Hopcroft Center for Computer Science
Shanghai Jiao Tong University
Email: wnzhang [AT] sjtu.edu.cn
Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.
This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.
Please submit the poster and coursework reports on time!
|Course work 1: Question-Answer Algorithms on Paper Reading
To select a better answer from the two answers given towards each abstract and question pair.
Apr. 3 - May. 14, 2019.
|Course work 2: Node Classification on Academic Network
To solve a multi-label classification problem in an academic network.
Apr. 10 - May. 29, 2019.
|Lecture 1: Introduction to Big Data Mining
Basic concepts, history and some examples of data mining.
Feb. 27, 2019
|Lecture 2: Know Your Data
Data representation, visualization and proximity measures.
Mar. 6, 2019
|Lecture 3: Fundamental Data Mining Algorithms
Frequent patterns, association rules, Apriori, FPGrowth, KNN.
Mar. 13, 2019
|Lecture 4: Supervised Learning (Part I)
Intro to machine learning, linear regression and logisitic regression.
Mar. 20 and 27, 2019
|Lecture 5: Supervised Learning (Part II)
Support Vector Machines, Neural Networks.
Apr. 3, 2019
|Lecture 6: Supervised Learning (Part III)
Tree models, Ensemble Methods
Apr. 3 and 10, 2019
|Lecture 7: Unsupervised Learning
K-means Clustering, PCA, Mixture Gaussian, EM Methods
Apr. 17, 2019
|Lecture 8: Search Engines
Information retrieval, inverted index, retrieval model, relevance model
May 8, 2019
|Lecture 9: Learning to Rank
Ranking problem, pairwise/listwise ranking, LambdaRank
May 15, 2019
|Lecture 10: Recommender Systems
Information filtering, collaborative filering, matrix factorization
May 15-22, 2019
|Lecture 11: Computational Ads
Computational advertising, auctions, sponsored search, contextual ads
May 22-29, 2019
|Lecture 12: Behavioral Targeting
Display advertising, RTB, Fruad detection
Jun. 5, 2019
- UIUC CS512 Data Mining: Principles and Algorithms. by Prof. Jiawei Han.
- Stanford CS229 Machine Learning course by Prof. Andrew Ng.
- Top 10 algorithms in data mining by Xindong Wu et al. 2008.
- Matrix Cookbook: fundamentals of matrix calculations.
|Haiwen Wang, 2018 Ph.D student at IIoT
Research on data mining, graph deep learning
Email: wanghaiwencn [at] foxmail.com
|Zhaorun Han, 2018 M.S. student at IIoT
Research on big data analysis
Email: hanzhaorun [at] sjtu.edu.cn
|EE448 Big Data Mining 2018
Compared to EE448 2018, EE448 2019 will provide more DM scenarios and advanced DM researches.
Mar. 1, 2019
Feb. 26, 2019