NEURAL COMPUTATION
CMPS 290D, Winter 2001
- Lecture times:
- TTh 4:00-5:45am, Social Sciences 2, 363
- Instructor:
- Prof. Manfred Warmuth (manfred@cse.ucsc.edu)
- Phone: (831) 459-4950
- Office: 331 Applied Sciences
- Office Hours: Mo 10-11, Th 12-1
Lecture 1:
General discussion of brains versus
artificial neural nets
Examples of how to communicate with
brain incuding
Armed Rats
Linearly least squares, Widrow Hoff
algorithm, gradient descent, perceptron algorithm,
feed forward neural nets
Lecture 2:
On-line versus batch,
single neuron with arbitrary increasing transfer function,
dot products, margins, Perceptron
Convergence Theorem, rotation invariance
Lecture 3:
Logistic regression, convexity, motivation
of relative entropy from coding theory,
logistic regression with square loss
leads to local minima, entropic loss
Lecture 4:
Matching Loss functions for single neurons,
exponenentially many minima if wrong loss is
used, motivation of the hinge loss for
learning with threshold functions,
loss functions for the multiclass case
Homework 1
Data is in /projects/learning/data/290D01
Homework 2
Voted Perceptron Paper
Homework 3
Paper in which
updates are derived
Homework 4
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