기계학습 (Machine Learning)

• 강의실: 공학 7관 xxx호
• Textbooks
  • Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [link] [pdf][korean]
• Textbooks (additional)
  • Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012 [link]
  • Kevin Patrick Murphy, Probabilistic Machine Learning: An instruction, MIT Press, 2022 [link]
  • Kevin Patrick Murphy, Probabilistic Machine Learning: An advanced topic, MIT Press, 2023 [link]
  • I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, 2016 [book]
  • Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998 [link] [2nd_edition]
  • D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 [book]
• References
  • Matrix cookbook pdf
  • Matrix differential calculus with applications in statistics and econometrics pdf
  • Matrix differentiation pdf
  • The Matrix Calculus You Need For Deep Learning pdf
  • Old and New Matrix Algebra Useful for Statistics pdf
  • Python notebooks for PRML text book [link]
  • ML Lecture at MIT [link]
  • ML Lecture at NYU [link]
  • ML and Data Mining Lecture at U. Toronto [link]
  • ML Lecture at U. Toronto [link]

Chapter 1: Introduction [pdf][pdf] [pdf]
• Chapter 2: kNN [pdf]
• Chapter 3: Decision trees [pdf]
• Chapter 4: Ensembles [pdf][pdf]
• Chapter 5-0: Probability distributions [pdf]
• Chapter 5: Linear models for regression [pdf]
• Chapter 6: Linear models for classification [pdf][pdf][pdf][pdf][pdf]
  • Tutorial: Naive Bayes and Gaussian Bayes Classier [pdf]
• Chapter 7: Neural networks [pdf][pdf]
• Chapter 8: Kernel methods
• Chapter 9: Sparse kernel machines [pdf][pdf]
• Chapter 10: Mixture models and EM [pdf][pdf]
• Chapter 11: PCA [pdf]
• Chapter 12: Matrix factorization [pdf]
• Chapter 13: Approximate inference [pdf]
• Chapter 14: Sampling methods [pdf]
• Chapter 15: Continuous latent variables
• Chapter 16: Sequential data
• Chapter 17: Combining models

• Assignment 1: [pdf]