Teaching Schedule

CS 778 – Winter 2016

Presenter	Topic	Model	Readings
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	ISOMAP/LLT	Req: 4.2-4 Opt: 4.1,4.5,4.6 Opt: MS t-SNE
Robert Stromberg	Genetic Programming - Slides	GP	Req: Genetic Programming pp. 250-275
Yao Chou	Advances in Deep Learning - Slides	R-CNN	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