Class: TTh 4-5:45, Earth & Marine Sciences B210
Office hours: Mo,We 11-12, E2-357
Prerequisite: CMPS 242 - Machine Learning
or a grad class in Bayesian Statistics
or consent of instructor
Recommended Textbook by Nicolo Cesa-Bianchi and Gabor Lugosi
Summary of lectures
1 Notes 1
On-line versus batch
Definition of regret
Halfing algorithm and its bound
Weighted majority algorithm
Regret bound via potential functions
Bug machine
Weighted Majority paper
2 Talk re. various Share Updates incl. one that induce longterm memory
Long term memory paper
Original "Tracking the best expert" paper
Talk re measuring on-lineness
Talk w. more details on Disk Spindown Problem
Original Disk Spindown paper Updated to journal version
Homework 1 Due Th April 12, beginning of class
Datasets
3 Talk re building caching strategies based on the shifting expert framework
Conference paper Master's thesis w. more details
Visualizations of relatie entropies Maple file
Motivation of relative entropy & relative loss bounds using relative entropy as measure of progress Notes 3
4 Combine two priority lists for caching w. Arcing Alg
Alternate method based on exponenential weights
Attempts to prove relative loss bounds
for the disk spindown problem Plots of hormonic weights Maple file
and combining heads of lists via exponential weights
Weighted Median algorithm for the 2-list case
Notes 4
Homework 2 Due Th April 19, beginning of class Clarified some of the problems
5 Combining heads from k lists
Relative loss bouds for shifting experts
Intro to co-learning Paper
Notes 5
6 More on co-learning
Lossfunctions other than the square loss paper
How does Bayesian analysis fit into the expert framework
Notes 6
7 Learning permutations with exponential weights
Intro about predicting the stock market Paper with EG algorithm
Notes 7
Homework 3 Due Tu, May 1, beginning of class
8 Experimental evaluation of stock market prediction algorithm by Ryan Weber
9 Sols to HW2 & 3
Status of open problems
Deriving GD, EG and Newton's update
Implicit vs Explicit updates
Notes 9 Made some corrections
10 Lagrangians and duality
How used it for proving bounds
Notes 10
Homework 4 Due Th, May 10, beginning of class
11 Online PCA talk paper
Notes 11
12 Sol HW4
More on PCA via learning as well as the best set of experts
Notes 12 Made some corrections
Homework 5 Due Tu, May 15
Claimed projects Add yourself
13 Relative loss bounds of GD and EG for linear regression
Linear regression with density matrix parameter
Tutorial re MRI application
Paper on finding density matrices with LLS
Notes 13
14 SVD LLS, Pseudoinverse,
Learning disjunctions
- inefficient alg with one expert per disjunction
- Winnow and its bound
Matrix version of Winnow based on SVD
Talk on symmetric to general matrix conversion
Notes 14
15 SVMs, kernels
Leaving the span
Notes 15
16 Rotation invariance
Notes 16
kpca
Blessing and curse of multiplicative update
17 Bregman divergences, Generalized Pythagorean Thm, Matching Loss, motivation via exponential families
Boosting talk w. emphasis on games
Boosting talk w. emphasis on Bregman divergences
TotalBoost paper
18 Multiplicative updates with kernels talk paper
Relative loss bounds for Temporal Difference Learning
Multiarm Bandit Problem with multiplicative updates
18' Gerry Tessauro's lecture on reinforcement learning
19 Bayesian probability calculus for density matrices talk paper
20 Project presentations (about 30 min each):
Corrie/Anindya
Adam
Jenniffer
Mo Rest of presentations in E2-489, 12pm
Seinjuti
Jessica
Ricardo
David
Mark/Karen
Alex
Projects due end of finals week