2301491 Special Topics in Computer Science

• แบบฝึกหัด

• แบบฝึกหัด

All slides

• Introduction
• Linear algebra, Probability, statistics and information theory, Numerical computation, Machine learning basics: Chapter 2-5 in the Deep Learning book

• Masashi Sugiyama. Introduction to Statistical Machine Learning. Morgan Kaufmann, 2015.
• Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.

• Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti A. Zadeh (Eds.). Feature Extraction: Foundations and Applications. Springer-Verlag Berlin Heidelberg, 2006.
• Samuel J. Gershmana and David M. Blei. A Tutorial on Bayesian Nonparametric Models. arXiv:1106.2697v2 [stat.ML], 2011.
• Charles Sutton and Andrew McCallum. "An Introduction to Conditional Random Fields", Foundations and Trends in Machine Learning. (4):4 2012, 267-373
• Leonard Kaufman and Peter J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, 2005.
• Rui Xu and Donald Wunsch II. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks. (16):3 May 2005, 645 – 678.
• Daniel D. Lee and H. Sebastian Seung. Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference. MIT Press. (2001) pp. 556–562.
• Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu. Evangelos Simoudis, Jiawei Han, Usama M. Fayyad (Eds.). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. (1996) pp. 226–231.
• Alexander J. Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and Computing 14 (2004) pp. 199–222.
• David M. Blei, Andrew Y. Ng and Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003) pp. 993-1022.

• 2016