CSC321 Home

 

Lectures, Readings and Due Dates

Optional Readings

Tutorials

Computing

Assignments

Tests

 

Anthony Bonner's Homepage:

www.cs.toronto.edu/~bonner

 

CSC321 Spring 2014 Lectures, Readings and Due Dates

The lectures are 11am - 1pm on Fridays in CC 2130.
Tentative Schedule:

  • February 21: No Lecture (reading week)

  • February 25: Assignment 2 is due.
  • February 28:
    Lecture 13: Learning without a teacher: Autoencoders and PCA
    (notes as .ppt) (notes as .pdf)
    Reading: ()
  • February 28:
    Lecture 14: Clustering: The EM algorithm for fitting mixtures of Gaussians
    (notes as .ppt) (notes as .pdf)
    Reading: ()

  • March 7: Midterm test, 11:10am - 12:00pm, in class.
  • March 7:
    Lecture 15: Mixtures of experts (This material will not be covered and will not be on the exam)
    (notes as .ppt) (notes as .pdf)
    Reading: Adaptive mixtures of local experts (.pdf)
  • March 7:
    Lecture 16: Hopfield Nets and simulated annealing
    (notes as .ppt) (notes as .pdf)
    Reading: For a gentle introduction to the idea of memories as energy minima (.pdf) (.html)
    Reading: For a gentle introduction to how to add new memories by creating new minima (.pdf) (.html)

  • March 11: Assignment 3 is posted.

  • March 14:
    Lecture 17: Boltzmann machines as probabilistic models
    (notes as .ppt) (notes as .pdf)
  • March 14:
    Lecture 18: Learning in Boltzmann machines
    (notes as .ppt) (notes as .pdf)
    Reading: Scholarpedia entry on Boltzmann machines (.pdf) (web page)

  • March 18: Assignment 3 is due.

  • March 21:
    Lecture 19: Learning Restricted Boltzmann Machines
    (notes as .ppt) (notes as .pdf)
  • March 21:
    Lecture 20: Learning features one layer at a time
    (notes as .ppt) (notes as .pdf)
    Reading for lectures 19 and 20: Optional extra reading for lectures 19 and 20: "Learning multiple layers of representation" (.pdf)

  • March 25: Assignment 4 is posted.

  • March 28:
    Lecture 21: Using backpropagation to fine-tune deep networks
    (notes as .ppt) (notes as .pdf)
    Reading for lecture 21: "Reducing the dimensionality of data with neural networks" (.pdf)
  • March 28:
    Lecture 22: Transforming Autoencoders for learning the right representation of shapes.
    (notes as .ppt) (notes as .pdf)
    Reading for lecture 22: "Transforming Autoencoders" (.pdf)