CSC2535 Spring 2008 - Lectures
LECTURE SCHEDULE: SUBJECT TO CHANGE
The lecture notes will generally be posted on the webpage around the time of the
lecture.
- January 9
Lecture 1: Graphical Models 1 [notes]
Required reading: Bishop, Chapter 8 pp 359-393
- January 16
Lecture 2: Graphical Models 2 [notes]
Required reading: Bishop, Chapter 8 pp 393-418
- January 23 (first assignment posted on web)
Lecture 3: Variational Methods [notes]
Required reading: Bishop, Chapter 10
- January 30
Lecture 4: Sampling Methods [notes]
Required reading: Bishop, Chapter 11
- February 6: First assignment due (at start of class)
Lecture 5: Energy-based models
notes as .htm ,
notes as .ppt ,
notes as .ps, 4 per page
Required reading: Contrastive backpropagation
[ps.gz]
[pdf]
Required reading: Energy-based models for sparse overcomplete representations.
[ps.gz]
[pdf]
- February 13 (second assignment posted on web)
Lecture 6: New advances in deep belief nets
notes as .htm ,
notes as .ppt ,
notes as .ps, 4 per page
Required reading:
- February 20 No Lecture (reading week)
- February 27 Second assignment due (at start of class)
Lecture 7: Models of words and documents [notes]
Required reading: Latent Dirichlet allocation.
[html]
- March 5
Lecture 8: Missing data problems and applications [notes]
Required reading: Learning from incomplete data.
[pdf]
Recommended reading: Collaborative filtering and the missing at random assumption.
[pdf]
- March 12
Lecture 9: Models for structured data (image and sequences) [notes]
Required reading: Conditional random fields: Probabilistic models for
segmenting and labeling sequence data.
[pdf]
Recommended reading:
Learning and incorporating top-down cues in image segmentation.
[pdf]
- March 19
Lecture 10: Non-linear Dimensionality Reduction
notes as .htm ,
notes as .ppt ,
notes as .ps, 4 per page
Required Reading: ISOMAP
article on web
Required Reading: Local Linear Embedding
[.pdf]
Required Reading: Stochastic Neighbor Embedding
[.ps]
Optional Reading: Aspect Maps
[.pdf]
Required Reading: t-SNE
[coming
soon]
- March 26
Lecture 11: Learning by maximizing agreement between outputs
notes as .htm ,
notes as .ppt ,
notes as .ps, 4 per page
Required Reading: A self-organizing neural network that discovers surfaces in random-dot stereograms.
[pdf]
Required Reading: Neighborhood Components Analysis
[pdf]
Required Reading: Learning a non-linear embedding by preserving class neighbourhood structure.
[pdf]
Optional Reading: Learning mixture models of spatial coherence
[pdf]
Optional Reading: Dimensionality Reduction by Learning an Invariant Mapping
[pdf]
- April 2
Lecture 12: Learning multiplicative interactions
notes as .htm ,
notes as .ppt ,
notes as .ps, 4 per page
Roland's lecture slides as .pdf
Required reading: Tenenbaum and Freeman 2000
pdf]
Required reading: Memisevic and Hinton 2007
pdf]
- April 9: Final Test (1.10pm-2.10pm)
- Tues April 15: Project due at Pratt 290D by 5.00pm
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