2019-1 Advanced Machine Learning
Time: Tue 13:00-16:00
Location: Cluster Bd. (학연산클러스터) R. 507
References: no textbook is required, but following references will be helpful:
- Elements of Statistical Learning, Springer, 2009 -- free PDF available
- Machine Learning, Murphy, MIT Press, 2012
- Infomation theory, inference, and learning algorithms, Mackay, Cambridge, 2017 (9th printing) -- free PDF available
- Foundations of machine learning, Mohri, Rostamizadeh, and Talwalar, MIT Press, 2018 (2nd Ed)
- All of statistics, Wasserman, Springer, 2005
- CS229 Stanford lecture by Andrew Ng
Grading:
- Homework/discussion, Midterm, Final Exam = 30%, 30%, 30%
- Attendance: 10%
Homework
- Homework 1 (due April 2)
- Homework 2 (due April 16)
- Homework 3 (due May 28)
Lecture Notes
Topics will be selected from learning models, learning theory, and large-scale optimization.
- Lecture 1. Introduction (pdf)
- Lecture 2. Supervised Learning (pdf)
- Linear Regression, Weighted Linear Regression, Perceptron, Exponential Family, Generalized Linear Models
- Suppl : Linear algebra review (pdf)
- Lecture 3. Generative Models (pdf)
- Gaussian discriminative analysis, naive Bayes, Laplace smoothing
- Suppl : Probability theory review (pdf)
- HW1 is out!
- Lecture 4. Support vector machines (pdf)
- Lecture 5. HW1 Discussion & Learning Theory part I (pdf)
- Suppl: Hoeffding's inequality (pdf)
- HW2 is out!
- Lecture 6. Learning Theory part 2
- Lecture 7. HW2 Discussion, SMO algorithm, Regularization and Model Selection (pdf)
- Midterm (April 23, in class)
- Lecture 8. EM algorithm (pdf1, pdf2)
- Bayesian inference (remainder of L7)
- Midterm review
- Lecture 9. Mixture of Gaussian, Factor Analysis (pdf)
- Lecture 10. PCA (pdf) and ICA (pdf)
- Eigenfaces
- HW3 is out!
- Lecture 11. Reinforcement learning and control (pdf)
- Lecture 12. LQR, DDP, and LQG (pdf)
- Lecture 13. POMDP, Policy Search, Reinforce, Pegasus
- Final Exam (June 11, in class)