Date |
Topics |
Slides |
9/1 Wed |
Course overview
|
9/6 Mon |
Motivating examples
Dynamical systems
|
9/8 Wed |
Dynamic learning
Visual storytelling project
| Ch1_Intro
|
9/13 Mon |
Autoregressive models
Time-series analysis
AR, MA, ARMA
Term project announced (First report due 10/13)
|
Ch2_AR
|
9/15 Wed |
Kalman filters
Model, algorithms, variants
|
Ch3_KF
|
9/20 Mon |
No class (Thanksgiving holiday)
|
9/22 Wed |
No class (Thanksgiving holiday)
|
9/27 Mon |
Monte Carlo filtering
Monte Carlo approximation
Importance Sampling (IR)
|
Ch4_PF
Supplement 1
Supplement 2
|
9/29 Wed |
Particle filters
Sequential Monte Carlo (SMC)
Vidio lecture : http://videolectures.net/mlss09uk_godsill_pf/
|
10/4 Mon |
Evolutionary Monte Carlo
EC, PSO, evolutionary MCMC, EDA
|
Ch5_EMC
Supplement 1
Supplement 2
Supplement 3
|
10/6 Wed |
Monte Carlo and the mind
Video lecture: http://videolectures.net/mlss2010_griffiths_mcm/
|
10/11 Mon |
Markov decision processes (MDPs)
Definition, problem, solution
Partially observable MDPs
|
Ch6_MDP_RL
|
10/13 Wed |
Reinforcement learning
Q-learning
Due of project report 1 is postponed for a week.
|
Ch6_MDP_RL
|
10/18 Mon |
Video lecture: Reinforcement learning
|
10/20 Wed |
Video lecture: Graphical models
Due of porject report 1
|
10/25 Mon |
Hidden Markov models (HMMS)
Model, learning, inference
|
Ch7_HMM
|
10/27 Wed |
Conditional random fields (CRFs)
Model, learning, inference
|
Ch8_CRF
|
11/1 Mon |
Dynamic Bayesian networks
Architecture, learning, inference
Video lecture: Dynamic factor graphs (DFGs)
|
Ch9_DBN
|
11/3 Wed |
Dynamic hypernetworks
Architecture, learning, inference
|
Ch10_DHN
|
11/8 Mon |
Discussion
|
11/10 Wed |
Discussion
|
11/15 Mon |
No Class
|
11/17 Wed |
Exam
|
11/22 Mon |
Project presentation 1
Bado Lee
Eun-Sol Kim and Myung-Goo Kang
Byoung-Kwon Lim
|
11/24 Wed |
Project presentation 2
Taemin Park, Joonso Lee and Dooyoung Kim
Minkyu Kim and Weerayot Aramphianlert
|
11/29 Mon |
Project presentation 3
|
12/1 Wed |
Future of dynamic learning
|
12/6 Mon |
Due of project report 2
|