Spring 2008. ICS 491: Special topics. T/R
1-2.15. FLYER
Neuroinformatics and Machine Learning:
From synapses to algorithms. An
Introduction.
Lecture |
Homework (tentative -- this
column will change) |
Overview
and Introduction. |
|
Computations
in single neurons:
Biophysical modeling (Hodgkin-Huxley model). (2) Film |
Homework 1 |
Single neurons and synnaptic
connections. Simple learning rules. (2) |
|
Supervised learning: The perceptron algorithm. | Homework 2 |
Feed-forward artificial neural
networks (ANNs): Backpropagation. (2) |
Homework 3 |
Recurrent ANN, associative
memory: Hopfield Network. (2) |
|
Worrying about generalization
error: Intro to VC-dimension, Structural risk minimalization and
Support vector machines (SVMs). SVM algorithm. (3) |
Homework (voluntary -- extra credit!) |
Unsupervised Learning and
Cluster analysis. K-means algorithm. (2) |
Homework 4 |
Bayesian Inference |
|
Time Series Analysis; Hidden
Markov Models. (2) |
|
Information theory: Uncertainty and Entropy. Conditional entropy and mutual information. (2) | |
Rate--distortion theory. Lossy compression through relevance. | |
Application to clustering;
Deterministic annealing; soft K-means
algorithm. (2) |
Homework 5 |
Complexity control. |
|
Prediction. Optimal causal
inference. |