HMC CS 152, Neural Networks

Spring 2014

Instructor: Prof. Robert M. Keller, B165 Olin, Office Hours: MWF 4:00-5:30 pm

Text

Thomas J. Anastasio, Tutorial on Neural Systems Modeling, Sinauer (2010), ISBN 978-0-87893-339-6

(Errata for the Text)

Term Projects

Biological Plausibility of Neural Network Models by Irina Rabkina and Jessica Schroeder

Phoneme Based Speech Recognition with Neural Networks by Chris Eriksen and Will Newbury

Analysis and Implementation of DropConnect Networks by Bill deRose and Daniel Weinand

Etch Figure Classification by Andrew Donelick

Neural Networks For Animation by Ari Hausman-Cohen

Hebbian Networks for Text Repair by JT Booth

NaGInI: Neural Networks Controller for a Generated Inverted pendulum Interface by Yukun Lin and Teo Asinari

Neural Networks and the Game of Pac-Man by Christine Schubert

Neural Network Vision for Robotic Driving by Jonathan Williams

Facial Expression Recognition Using Deep Learning by Hayden Blauzvern

Learning TTT and other Simple Games by James Bowen

League of Legends by Jenner Felton

Predicting the Biological Gender of Twitter Users by Jordan Varney and Isabella Funke

Distributed Backpropagation Neural Network by James Reinke

Why Neuro-fuzziness? A search in literature by Huameng Jiang

Determining Player Value from Box Score Statistics in Basketball by Ethan Kenny

Categorizing Email Importance by Casey Chu

Twitter Recommendation Engine by Maury Quijada

Political Bias Detecting Neural Net by Perry Holen

Neural Network Implementation of Blackjack AI by Corey Hayes and Elsie Gibson

Preprocessing and Neural Networks by Zach Siegel

An analysis on the viability of Neural Network AI in Rock, Paper, Scissors by Henry Huang

Scalar Boltzmann Machine by Lloyd Cloer

Lecture Slides & Assignments

Introduction (includes first assignment)

Neuron Details

Perceptron Model

Assignment 2: Perceptrons

data02.zip (data for Assignment 2)

Simple Recurrent Networks

Adalines

Backpropagation

Backpropagation Demos

Assignment 4: Backpropagation

Backpropagation Applications

Backpropagation Tricks and Techniques

Backpropagation Variations and Optimizations

Radial Basis Function Networks

Lateral Inhibition Networks

Competitive Learning

Self-Organizing Maps

Reinforcement Learning and Associative Conditioning

Models related to Hopfield Nets

Reinforcement Learning and Temporal Difference Method

Recurrent Backpropagation

More on Time and Neural Networks

Support Vector Machines