Class time

May 12 - August 9, 2021

Instructor and TAs

About this course

What should you do when you are facing a huge amount of complicated data from real life applications? This course introduces the core techniques in big data analytics, namely knowledge discovery in databases (KDD), also known as data mining (DM). It focuses on the principles, fundamental algorithms, implementations, and applications.

Prerequisites

• Comprehensive understanding and fluent skills in data structures, such as linked data structures, B-trees, and hash functions.
• Analysis of algorithms and time complexity.
• Operating systems, main memory and disk management, file systems.
• Elementary probability theory and statistics, such as random variables, distributions, probability mass functions, sampling, and statistical tests.

Textbook and references

• (Official textbook) Data Mining: Concepts and Techniques (3rd ed.), Jiawei Han, Micheline Kamber, and Jian Pei, Morgan Kaufmann, 2011.
• Drafts of some chapters in the 4th ed. will be available for interested students.

Format and Zoom link

• A combination of live zoom meeting sessions and pre-recorded lectures. Students are required to attend the live sessions. See the schedule section for details.
• Live sessions of classes through zoom CMPT 741 Zoom.
• Instructor’s office hours through zoom Jian’s office hour zoom.
• TAs’ office hours through zoom TAs’ office hour zoom.
• In-class exams.

Course outline

1. Welcome and introcution
2. Mining patterns and rules
3. Predictive analytics
4. Clustering analytics
5. Outlier detection
6. Advanced topics

Assignments (tentative, subject to changes)

Schedule (tentative, subject to changes)

References for course project

• Data/model markets & pricing
  • Pei, J. (2020). A Survey on Data Pricing: from Economics to Data Science. ArXiv, abs/2009.04462. [Podcast] [KDD 2020 tutorial]
• Interpretability
  • W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu, Definitions, methods, and applications in interpretable machine learning, Proceedings of the National Academy of Sciences Oct 2019, 116 (44) 22071-22080; DOI: 10.1073/pnas.1900654116
  • Carvalho, D.V.; Pereira, E.M.; Cardoso, J.S. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics 2019, 8, 832. https://doi.org/10.3390/electronics8080832
• Fairness/bias
  • Mehrabi, Ninareh et al. “A Survey on Bias and Fairness in Machine Learning.” ArXiv abs/1908.09635 (2019)
  • Caton, Simon and C. Haas. “Fairness in Machine Learning: A Survey.” ArXiv abs/2010.04053 (2020).
  • S. Barocas, M. Hardt, and A. Narayanan. “Fairness and Machine Learning”.