CS 5970/5043 Advanced Machine Learning Class schedule

The following is a schedule of the lectures and presentations for CS 5043 Advanced Machine Learning for Spring 2008. This schedule will be updated throughout the semester. All readings are from Pattern Recognition and Machine Learning by Christopher Bishop unless otherwise noted.

Date Topic Assigned Reading Assigned Due
Jan 15 (Week 1: Intro and Class Design) Introduction/Class design/Undirected Graphical Models 8.3    
Jan 17 Finalized course design/Undirected graphical models      
Jan 22 (Week 2: Advanced graphical models) Hidden Markov Models 13.1-13.2    
Jan 24 : Abstract Hidden Markov Models Paper   Summary 1
Jan 29 (Week 3: Advanced graphical models) Dynamic Bayesian Models

Paper, Additional reading available here

  Summary 2
Jan 31 Exact and approximate inference 8.4.3, 8.4.4    
Feb 5 (Week 4: Advanced graphical models) Non-parametric density estimation Thrun's Ch 4 (on D2L)   Summary 3
Feb 7 Using graphical models for robot localization      
Feb 12 (Week 5: Advanced Graphical models) Particle filtering for robots : real-world particle filtering Thrun's Ch 5 (on D2L    
Feb 14 Relational learning: Relational Probability Trees Neville's paper   Summary 4
Feb 19 (Week 6: Advanced Graphical Models) Relational learning: Spatiotemporal Relational Probability Trees Paper on D2L    
Feb 21 Relational learning: Relational Dependency Networks Neville's paper   Summary 5
Feb 26 (Week 7: Advanced Graphical Models) MCMC and Gibbs Sampling and RDNs Optional paper on MCMC on D2L    
Feb 28 Project 1 presentations      
Mar 4 (Week 8: Kernel Methods) Introduction to kernel methods Chapter 6: 6-6.2    
Mar 6 Kernels 6.3 Project 2 Summary 6 (due on all of Chapter 6)
Mar 11 (Week 9: Kernel Methods) Radial Basis Functions, Project proposals, Support Vector Machines Chapter 7.1   Project 2 proposals
Mar 13 Support vector regression Chapter 7.1 continued   Summary 7
Mar 15-23
Spring Break!
Mar 25 (Week 10: Kernel Methods) Support Vector regression Bishop 7.1   Project 2 status
Mar 28 Least Squares SVM and SVRs Suykens paper on D2L    
Apr 1 (Week 11: Advanced Reinforcement Learning) Hierarchy: Options Precup's paper on D2L Project 3 Summary 8
Apr 3 Project 2 presentations     Project 2
Apr 8 (Week 12: Advanced Reinforcement Learning) Project 2 presentations, Project 3 proposals Dietterich's paper on D2L   Summary 9, Project 3 proposals
Apr 10 Hierarchy: Max-Q      
Apr 15 (Week 13: Advanced Reinforcement Learning) Learning abstractions and hierarchy automatically Hengst and McGovern papers on D2L   Summary 10
Apr 17 Learning other forms of abstraction: Proto-RL Paper   Summary 11
Apr 22 (Week 14: Advanced Reinforcement Learning) Proto RL     Project 3 status
Apr 24 Practical function approximation Ng paper on D2L    
Apr 29 (Week 15: Course wrapup) Intrinsic motivation Simsek paper on D2L   Summary 12
May 1 Transfer learning Konidaris paper on D2L   Summary 13
May 7
Final exam 4:30-6:30: Project 3 presentations