General Information

• • Class Time: Mon 12:00~13:15 and 13:30~14:45

  • Location: 26419 (Engineering Building 2)

  • Language: Korean

Instructor

• • Prof. Seokho Kang

  • Office: 27408B (Engineering Building 2)

  • E-mail: s.kang@skku.edu

  • Office Hours: Mon 13:30~15:00 or by appointment

Teaching Assistant

• • Mr. Kyoham Shin and Ms. Suhee Yoon

  • Office: 27407 - Data Mining Lab. (Engineering Building 2)

  • E-mail: 21khaiden@gmail.com and suhee.y66@gmail.com

  • Office Hours: after class

References

• • Pang-Ning Tan, Michael Steinbach, Anuj Karpatne & Vipin Kumar, Introduction to Data Mining (2nd ed.), Pearson, 2019. (https://www-users.cs.umn.edu/~kumar001/dmbook)

  • Jiawei Han, Micheline Kamber & Jian Pei, Data Mining: Concepts and Techniques (3rd ed.), Morgan Kaufmann Publishers, 2011. (http://hanj.cs.illinois.edu/bk3)

  • https://github.com/stepthom/data_mining_resources

  • https://github.com/RatulGhosh/awesome-machine-learning

Evaluation

• • Attendance (10%)

  • Assignments (10%)

  • Presentation (10%)

  • Mid-term Exam (35%)

  • Final Exam (35%)

  • Total (100% + a)

Schedule (Subject to Change)

• • Syllabus [download] (last updated on 3/24)

  • Note: ALL classes (except mid-term and final exams) will be conducted online using icampus (https://icampus.skku.edu/)

Lecture Notes + Further Readings

* Most of the slides are adapted from Pang-Ning Tan’s materials, which are available at https://www-users.cs.umn.edu/~kumar001/dmbook

• • [3/9] Course Introduction [download] (last updated on 3/9)

    • 1. Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, 39(12), 11303-11311.

      2. Wu, X., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.

  • [3/16] 1. Data [download] (last updated on 3/9)

    • 1. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.

      2. Lin, W. C., & Tsai, C. F. (2019). Missing value imputation: a review and analysis of the literature (2006–2017). Artificial Intelligence Review, 1-23.

  • [3/23] 2. Classification: Basic Concepts [download] (last updated on 3/9)

    • 1. Kuhn, M., & Johnson, K. (2013). Over-fitting and model tuning. In Applied predictive modeling (pp. 61-92). Springer, New York, NY.

      2. Domingos, P. (2000). A unified bias-variance decomposition and its applications. In Proceedings of 17th International Conference on Machine Learning (pp. 231-238).

      3. Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11(Jul), 2079-2107.

  • [3/30] 3. Classification: Algorithms - Part 1 [download]

    • 1. Bottou, L., & Vapnik, V. (1992). Local learning algorithms. Neural computation, 4(6), 888-900.

      2. Garcia, S., Derrac, J., Cano, J., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 417-435.

  • [4/6] 3. Classification: Algorithms - Part 2 [download]

    • 1. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167.

      2. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222.

      3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

      4. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.

  • [4/13] 3. Classification: Algorithms - Part 3 [download]

    • 1. Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39.

      2. Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221-232.

      3. Kang, S. (2020). Model validation failure in class imbalance problems. Expert Systems with Applications, 113190.

      4. Hand, D. J. (2006). Classifier technology and the illusion of progress. Statistical Science, 1-14.

  • [5/4] 4. Cluster Analysis - Part 1 [download]

    • 1. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.

      2. Dhillon, I. S., Guan, Y., & Kulis, B. (2004). Kernel k-means: spectral clustering and normalized cuts. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 551-556).

  • [5/11] 4. Cluster Analysis - Part 2 [download]

    • 1. Kriegel, H. P., Kröger, P., Sander, J., & Zimek, A. (2011). Density‐based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 231-240.

      2. Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and computing, 17(4), 395-416.

      3. Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 25(3), 337-372.

  • [5/18] 5. Anomaly Detection [download]

    • 1. Tax, D. M. (2001). One-class classification. PhD thesis, Delft University of Technology.

      2. Tax, D. M., & Duin, R. P. (2004). Support vector data description. Machine Learning, 54(1), 45-66.

      3. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58.

      4. Khan, S. S., & Madden, M. G. (2014). One-class classification: taxonomy of study and review of techniques. Knowledge Engineering Review, 29(3), 345-374.

  • [5/25] 6. Association Analysis [download]

    • 1. Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1), 55-86.

      2. Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9-es.

  • [6/15] Final Exam

Supplementary Materials

• • [4/20] Paper Presentation 1 [download]

  • [4/27] Special Issues in Industrial Artificial Intelligence: Overcoming Data Scarcity

  • [6/1] Paper Presentation 2 [download]

  • [6/8} Invited Talk by Dr. Inbeom Park, Postdoctoral Associate at Seoul National University

Presentation

Each student will present at least one research paper in class (give a 10-min presentation). The objective is to share recent research advances or applications of industrial artificial intelligence in real-world problems in industrial engineering fields.

Submit your presentation video (10-min) and material to the instructor and TA at least one day before your presentation (e-mails: s.kang@skku.edu and 21khaiden@gmail.com)

[4/20] Paper Presentation I (12 students): 강현구, 박일하, 박희영, 백승민, 신교함, 이규원, 이해인, 이형준, 전용석, 최예림, 최원화, 한종민

[6/1] Paper Presentation II (12 students): 강명인, 강문식, 김민환, 김재호, 김지수, 김진주, 김한민, 김혜인, 김현찬, 송승현, 심아름, 이세진

Assignments

All assignments must be submitted to icampus (https://icampus.skku.edu/) by midnight of the due date. Late submissions will NOT be accepted.

• • [A1] Self-introduction Report (due date: 3/30): introduce yourself within 1 page. include the following: your name, student ID, personal history, your research areas/interests, what do you expect to learn from this course?, suggestions/comments to the instructor, miscellaneous.

  • [A2] Research Proposal (due date: 6/15): write a research proposal within 3 pages. the topic must be relevant to what you have learned or will learn from this course.