Lecture 2: Bayesian Decision Theory I
Bayesian decision theory is the basic framework for pattern recognition.
Outline:
1. Diagram and formulation
2. Bayes rule for inference
3. Bayesian decision
4. D
Lecture 5-6: MDS, LLE, Intrinsic dimensions
Multi-dimensional scaling
MDS is a technique motivated by 2-problems in understanding data in high dimensional spaces.
Its objective is to project an ensemb
Lecture 7-9. AdaBoost, RealBoost, and Logistic Regression
Boosting: One of a few popular machine learning algorithms in last 15
years (the others are Support Vector Machines, and DeepLearning).
It has
Stat231-CS276A
Pattern Recognition and Machine Learning
Lecture note, Stat231-CS276A,
S.C.Zhu
Recent advances in PRML
IBM Watson for Jeopardy
1, Understand human speech
2, Search and evaluates hypoth
Stat 231 / CS 276A
Pattern Recognition and Machine Learning
Fall 2017, MW 2:00-3:15 PM, Physics and Astronomy Building 1434A
www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html
Course Descrip