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CSC2535 Spring 2011 - Lectures
DRAFT LECTURE SCHEDULE: SUBJECT TO CHANGE
The lecture notes will generally be posted on the webpage around the time of the
lecture.
- January 12
Lecture 1: Overview of Machine Learning and Graphical Models
notes as ppt ,
notes as .pdf
Reading: Bishop, Chapter 8: pages 359-418
- January 19
Lecture 2: Approximate Inference and Learning in Directed Graphical
Models
notes as ppt ,
notes as .pdf
Reading: Bishop, Chapter 10: pp 461-505
- January 26 (first assignment posted on web)
Lecture 3a: Approximate Learning Methods for Energy-Based Models
notes as ppt ,
notes as .pdf
Lecture 3b: Learning Deep Boltzmann Machines
notes as .odp ,
notes as .pdf
Required reading: Training Products of Experts by Minimizing Contrastive Divergence.
[pdf]
Required reading: Training Restricted Boltzmann Machines using Approximations to
the Likelihood Gradient.
[pdf]
Required reading: Estimation of non-normalized statistical models using score matching.
[pdf]
- February 2
Lecture 4: Methods for Sampling from Distributions
notes as .ppt ,
notes as .pdf
Reading: Bishop, Chapter 11: pages 523-558
- February 9: First assignment due (at start of class)
Lecture 5a: More ways to fit energy-based models
notes as ppt ,
notes as .pdf
Lecture 5b: Object Recognition and Information Retrieval in Deep Belief Nets
notes as ppt ,
notes as .pdf
Required reading: Reducing the dimensionality of data with neural networks.
[pdf]
Required reading: Small codes and large image databases for recognition.
[pdf]
Required reading: Using Very Deep Autoencoders for Content-Based Image Retrieval
[pdf]
Optional reading: A Fast Learning Algorithm for Deep Belief Nets.
[pdf]
- February 16 (second assignment posted on web)
Lecture 6a: Learning Three-Way Interactions for Image Transformations and
Animate Motion.
notes as .ppt ,
notes as .pdf
Required reading: Learning to represent spatial transformations with
factored higher-order Boltzmann machines.
[pdf]
Required reading: Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style.
[pdf]
Lecture 6b: Modeling character strings with recurrent neural networks
notes as .pdf
Required reading: Hardcopy to be handed out in class.
- February 23 No Lecture (reading week)
- March 2 Second assignment due (at start of class)
Lecture 7: Models of words and documents
Part 1 as .pdf
Part 2 as .pdf
Required reading: Latent Dirichlet allocation.
[html]
- March 9
Lecture 8: Generative models for language,
speech, and static images.
Language models: notes as .pdf
.....Required reading: [.pdf]
Speech recognition and image patch modeling: notes as .pdf
notes as .ppt
.....Required reading: [.pdf]
Much more on modeling the covariance structure of images:
notes as .pdf
.....Required reading: [.pdf]
- March 16
Lecture 9: Non-linear Dimensionality Reduction
notes as .ppt ,
notes as .pdf
Required Reading: ISOMAP
[article on web]
Required Reading: Local Linear Embedding
[.pdf]
Required Reading: Stochastic Neighbor Embedding
[.ps]
Required Reading: Visualizing Data using t-SNE
[.pdf]
Optional reading: A paper that relates SNE to Laplacian Eigenmaps [.pdf]
Optional reading: Dimension Reduction: A Guided Tour.
[.pdf
]
- March 22
Lecture 10: Collaborative filtering and Missing Data Problems
notes as .pdf
Required reading: Learning from incomplete data.
[pdf]
Recommended reading: Collaborative filtering and the missing at random assumption.
[pdf]
- March 30
Lecture 11a: Priors and Prejudice
notes as .ppt ,
notes as .pdf
Lecture 11b: Adaptation at multiple time scales
notes as .ppt ,
notes as .pdf
- April 6
Final Test
(1.10pm-2.10pm in the same place as the lectures)
Lecture 12 (2.20pm-3.00pm): The right way to recognize shapes
- Friday April 20: Project due at Pratt 290G by 5.00pm
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