Teaching Schedule

CS 778 – Winter 2016






Chris Tensmeyer

Probabilistic Regression - Slides

Gaussian Processes

Req: 2.1-2.5,  Opt: 18.1-18.2 Marsland book

Ethan Garofolo

Topic Modeling - Slides

Anchor Words (and review of LDA)

Req: Wiki Probabilistic Topic Models

Paul Bodily

Concept Learning - Slides

Hierarchical Bayesian Program Learning

Req: Version Space pp. 20-39, Opt: BayesianCL

Joseph Johnson

Multi-Agent Learning - Slides

Multi-Agent Clustering

Req: Multi-Agent Clustering  Opt: MAS

Stanley Fujimoto

LSTM - Slides

Gated Recurrent Unit

Req: LSTM RNN review section 4.1

Mike Brodie

Conditional Random Fields/SP - Slides

Linear-Chain Conditional Random Fields

Req: CRFs

Jeff Andersen

Spiking Neural Networks - Slides

RBF trained with SpikeProp

Req: Spiking NNs 1.1, 1.2, 1.3.2, 1.4, 1.5.2

Pei Guo

Non-Linear Dim Reduction - Slides


Req: 4.2-4 Opt: 4.1,4.5,4.6 Opt: MS  t-SNE 

Robert Stromberg

Genetic Programming - Slides


Req: Genetic Programming pp. 250-275

Yao Chou

Advances in Deep Learning - Slides


Req: DNN  Ch. 5, Opt: Hierarchies

Logan Mitchell

Connectionist Temporal Classification - Slides

RNNs with Automatic Sequence Segmentation

Req: Graves

Nozomu Okuda

Transduction - Slides

Transductive SVM

Req: Transduction  Opt: Semi-Sup Transduction