Machine Learning Study

Course Information
  • Instructor: Joonseok Lee, Seungyeon Kim
  • Time: Mon/Wed 13:00 - 14:30
  • Location: KACB 1305
  • Textbook
    • C. Bishop. Pattern Recognition and Machine Learning, Second Edition, Springer 2007.
    • T. Hastie et. al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer 2009.
    • R. Sutton and A. Barto. Reinforcement Learning: An Introduction. The MIT Press 1998.
  • Link to Qualifier Info
  • Link to Previous Exams

Schedule
Date Topic Presenter Readings
7/18 Mon Probability Theory, Model Selection Joonseok Lee PRML 1.1 - 1.3
7/20 Wed Decision Theory, Information Theory Seungyeon Kim PRML 1.4 - 1.6
7/25 Mon Binary/Multinomial/Gaussian Distribution Joonseok Lee PRML 2.1 - 2.3.4
7/27 Wed Gaussian Distribution, Exponential Family Seungyeon Kim PRML 2.3.5 - 2.4
8/1 Mon Nonparametric Methods, Linear Basis Function Models Joonseok Lee PRML 2.5 - 3.2
8/3 Wed Bayesian Linear Regression, Evidence Approximation Seungyeon Kim PRML 3.3 - 3.6
8/8 Mon Recess
8/10 Wed Recess
8/15 Mon Discriminant Functions, Generative Models Joonseok Lee PRML 4.1 - 4.2
8/17 Wed Discriminative Models, Bayesian Logistic Regression Seungyeon Kim PRML 4.3 - 4.5
8/22 Mon Neural Networks, Backpropagation Algorithm Joonseok Lee PRML 5.1 - 5.3
8/24 Wed Hessian Matrix, Regularization in Neural Networks Seungyeon Kim PRML 5.4 - 5.5
8/29 Mon Bayesian Neural Networks, Kernels Joonseok Lee PRML 5.6 - 6.2
8/31 Wed RBF Kernel, Gaussian Process Seungyeon Kim PRML 6.3 - 6.4
9/5 Mon Recess (Labor day)
9/7 Wed Support Vector Machine (SVM)
Relevance Vector Machine (RVM)		 Joonseok Lee
Seungyeon Kim		 PRML 7.1 - 7.2
9/12 Mon Recess
9/14 Wed Bayesian Networks, Markov Random Fields Joonseok Lee PRML 8.1 - 8.3
9/19 Mon Inference in Graphical Models, Factor Graphs Seungyeon Kim PRML 8.4
9/21 Wed K-means, Mixture of Gaussian Joonseok Lee PRML 9.1 - 9.2
9/26 Mon Recess
9/28 Wed EM Algorithms Seungyeon Kim PRML 9.3 - 9.4
10/3 Mon PCA, Probabilistic PCA Joonseok Lee PRML 12.1 - 12.2.2
10/22 Fri Kernel PCA, Continuous Latent Variables Joonseok Lee PRML 12.2.3 - 12.4
10/26 Wed HMM Seungyeon Kim PRML 13.1 - 13.2
11/7 Mon Linear Dynamical Systems Joonseok Lee PRML 13.3
11/9 Wed Combining Models Joonseok Lee PRML 14.1 - 14.5
11/14 Mon Basic Sampling Methods, MCMC Seungyeon Kim PRML 11.1 - 11.2
11/16 Wed Gibbs Sampling, Hybrid Monte Carlo Seungyeon Kim PRML 11.3 - 11.6