Statistical Methods for Intelligent Information Processing (3 credits)
Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333
Classroom: CSIE 111
Meeting Time: Tue 14:20-17:20 pm
Office Hour: After class or by appointment
TA: TBA
Course Description:
This course teaches how to process
information intelligently using statistical methods and algorithms.
Grading:
Programming Assignments: (60%)
Final Project: (40%)
Reference books:
Syllabus (tentative):
16-Sep | introduction+Basic |
Supervised Learning | |
23-Sep | Regression, DT, ME |
30-Sep | VC dimension, SVM, Lazy Learning |
7-Oct | HMM, , Bayesian |
14-Oct | Imbalanced Data Classification |
Unsupervised Learning | |
21-Oct | LM+viterbi |
28-Oct | EM |
4-Nov | EM+clustering |
11-Nov | Labelling |
Reinfocement learning | |
18-Nov | Monte Carlo, MDP |
25-Nov | Q-learning |
2-Dec | Project Proposal |
9-Dec | SARSA |
Machine Discovery | |
16-Dec | Advanced LM |
23-Dec | Discovery in Social Network |
30-Dec | Advanced topics in KDD |
6-Jan | Final Project Presentation |
13-Jan | Final Project Presentation |
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