January 10
Lecture 1: What are neural networks?
(notes as .ppt )
(notes as .pdf))
Reading: How neural networks learn from experience
( .pdf)
January 12
Lecture 2: Two simple learning algorithms
(notes as .ppt )
(notes as .pdf))
Reading: Connectionist Learning Procedures, pp 185-190; 193-198.
( .pdf)
January 17
Lecture 3: Learning in multilayer networks
(notes as .ppt )
(notes as .pdf))
Reading: Connectionist Learning Procedures, pp 198-205.
( .pdf)
January 19
Lecture 4: Learning to model relationships and word sequences
(notes as .ppt )
(notes as .pdf))
Reading: A neural probabilistic language model.
(.ps file)
(a paper written for researchers about predicting the next word in a
sentence).
January 24: (Assignment 1 will be posted)
Lecture 5: Distributed representations
(notes as .ppt )
(notes as .pdf))
Reading: Book chapter on "Distributed Representations"
( .pdf)
Reading: Scientific American article on "Simulating Brain Damage"
( .pdf)
January 26
Lecture 6: Applying backpropagation to shape recognition
(notes as .ppt )
(notes as .pdf))
Required viewing: http://yann.lecun.com
January 31: Assignment 1 due (at start of class)
Lecture 7: Learning in recurrent networks
(notes as .ppt )
(notes as .pdf))
Reading: Learning internal representations by error propagation, pp 354-362.
(
.pdf)
February 2
Lecture 8: Modeling text using a recurrent neural network trained with a really fancy optimizer
(notes as .ppt )
(notes as .pdf))
Reading: Generating text with recurrent neural networks (hardcopy will
be handed out in class)
March 1 Assignment 2 due (at end of class)
Lecture 13: Learning without a teacher: Autoencoders and PCA
(notes as .ppt )
(notes as .pdf )
Reading: ()
March 6: (Assignment 3 will be posted)
Lecture 14: Clustering: The EM algorithm for fitting mixtures of Gaussians
(notes as .ppt )
(notes as .pdf )
Reading: ()
March 13
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 ( .ps )
( .html )
Reading: For a gentle introduction to how to add new memories by creating new
minima ( .ps )
( .html )
March 15: Assignment 3 due (at start of class)
Lecture 17: Boltzmann machines as probabilistic models
(notes as .ppt )
(notes as .pdf )
March 22 Lecture 19 is in the tutorial slot and is required for the
final exam
Lecture 19: Learning Restricted Boltzmann Machines
(notes as .ppt )
(notes as .pdf )
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 27: (Assignment 4 will be posted)
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 29
Lecture 22: Transforming Autoencoders for learning the right representation
of shapes.
(notes as .ppt )
(notes as .pdf )
Reading for lecture 22: "Transforming Autoencoders" .pdf
April 3: Assignment 4 due (at start of class)
Lecture 23: Support Vector Machines: Part 1
(notes as .ppt )
(notes as .pdf )