COLT
Note: Slides will be updated every week
Final Exam: January 12th
Assignment
2: Deadline December 29th
· What
is learning? What is Learning? Learning from
examples, Proper Learning, Non-Proper Learning, Rays, Halfspace,
Threshold
· Learning From Examples,
Term, Duality, DNF, CNF, Monotone-DNF, k-DNF, k-term DNF, Compressed
hypothesis, Polygon.
· Composition Theorem,
RO-DNF, Formula, MFormula, DFA, Decision List, k-DL,
Decision Tree, Quasi-polynomial time and Sub-exponential time learning.
· Online Learning, Exact learning, Term,
Duality, Composition, WINNOW, Decision List, Decision Tree, Halving,
Perceptron, Text Classification, Expert Advice.
· PAC Learning, Definitions, Rays,
OCCAM, Term, k-DNF, k-term DNF
· PAC
learning, Markov, Chebychev, Sauer, Shatter
Coefficient, VCdim, epsilon-net, OCCAM
· Boosting, Chernoff,
applications, Weak learning, Boosting, Ada Boost.
Lecture Notes
Preliminaries
Advance Probability
VCdim and Sauer Lemma, Net and Sample (under construction)
COLT Models
Boolean Functions, DNF, CNF, Decision
Tree (DT), Decision List (DL)
DT DNF and DL, DFAutomata
(DFA), Multiplicity Automata (MA)
Online Learning
Online, Halving,
MTerm, Composition Thm.,
k-CNF, k-term DNF
XOR, Decision
List, Decision Tree, DNF, WINNOW
Results in other Online
Learning Models (under construction)
PAC Learning
The
PAC Model (under construction)
Simple Learning
Algorithms
Learning
using Divide and Conquer (under construction)
E
Learning CDNF, Decision Tree, O(log n)-term DNF
Multiplicity
Automata (under construction)
E
Learning
From Membership Queries (under construction)
Relationship
between Models
CH=PAC
(under construction)
Hardness Results
Learning
Read Thrice DNF (under construction)
References (under construction)
Late
Example of
Latex file, How to change it to
.ps and .pdf file, The pdf file
Papers