🔥 CISC873 Data Mining 🔥

[Current meme winner: Tyler Mainguy @tylermainguy - Checkout the competition]

Originally created by Prof. David Skillicorn in 2000

Instructor: Dr. Steven Ding

Friday 10:00-11:15 11:45-13:00 EST Miner Party - Live Streaming [Zoom link on Onq]

✨ [JOIN our discord channel HERE]✨

Course Intro Video

Study of the extraction of concepts from large high-dimensional datasets. Statistical foundations; techniques such as supervised neural networks, unsupervised neural networks, decision trees, association rules, Bayesian classifiers, inductive logic programming, genetic algorithms, singular value decomposition, hierarchical clustering.

Topics. Introduction to Data Mining. Overview of data mining, functions in data mining, the epistemology of data science, experimental protocol, performance assessment, ethical & privacy implications, human bias in data mining, introduction to Python for data science.

Tabular and Relational Data Mining. Experiment design, data preprocessing, data visualization, data exploration, linear models, performance evaluation, overfitting, bias-variance analysis, error estimation, tree-based methods, ensemble methods, instance-based learning, graph-based models, introduction to neural network & autograph, model tuning workflow, clustering.
Time Series, Transactional, and Sequential Data Mining. Time-series forecasting, association rule mining, sequential data mining, text data mining, recurrent neural networks, attention mechanisms, transformers, explainability, interpretability, topic embedding, representation learning.
Graph and Web Data Mining. Social network analysis, heterogeneous information network, graph-based representation learning, link prediction, graph alignment, meta-path analysis, recommendation systems, recursive neural network, graph neural network, multi-task learning.

Textbooks and references:

[Optional] Data Mining: Concepts and Techniques, 3rd Edition, by Jiawei Han, Micheline Kamber, and Jian Pei, Morgan Kaufmann, 2012. ISBN 978-0-12-381479-1.
[Optional] Zaki, M. J., & Meira, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.
[Optional] VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.".
[Required] PowerPoint slides, coding templates, and other online resources provided by the instructor.

Where to Ask a Question? ❓

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Assignments 🔨

Project Meeting Sign-up

(!!!Zoom Meeting link in the above shared sheet!!!)

[Timelines are Tentative, please let me if you have any conflicts!]

Assignments (Option #1 100% total)	Due Date	Individual Project (Option #2 100% total)	Due Date
Assignment #0	10-07	Project Proposal	10-02
Assignment #1	10-17	Phase 1 source code and results	10-23
Assignment #2	11-06	Phase 2 source code and results	11-13
Assignment #3	11-15	Phase 3 source code and results	11-27
Assignment #4	on Kaggle	Final report	12-18
Assignment #5	on Kaggle

Course Materials. 📚

Lecture Recordings & Notes 💪

Week	Sildes	Videos
Week #1 Introduction	Slides	L1-1 L1-2
Week #2 Regression	Slides	L2-1 L2-2
Week #3 Classification	Slides	TBA
TBA	TAB	TBA
Week #9	Slides	L9
Week #10	Slides	L10
Week #11	Slides	L11
Week #12		L12
Week #13		L13