Category Archives: Lectures

Lectures

Lecture 25, April 11th, 2016: More Generative Neural Nets

This is the last lecture. You are encouraged to ask questions about anything we have discussed during the term in order to better prepare for the exam.

Please also study the following material in preparation for the class:

  • End of Chapter 20 (sec. 20.11 to 20.15) of the Deep Learning Textbook (deep generative models).
  • Slides on deep generative modeling (42 to 59)
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Lectures

Lecture 24, April 7th, 2016: Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs)

In this lecture we will discuss two modern generative models, the variational auto-encoders (VAEs) and the generative adversarial networks (GANs).

Please study the following material in preparation for the class:

  • Part of Chapter 20 (sec. 20.9 to 20.10) of the Deep Learning Textbook (deep generative models).
  • Slides on deep generative modeling (26 to 41)

 

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Lectures

Lecture 23, April 4th, 2016: Deep Belief Networks and Deep Boltzmann Machines

In this lecture we will continue our discussion of probabilistic undirected graphical models with the Deep Belief Network and the Deep Boltzmann Machine.

Please study the following material in preparation for the class:

  • Part of Chapter 20 (sec. 20.1 to 20.8) of the Deep Learning Textbook (deep generative models).
  • Slides on deep generative modeling (1 to 25)
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Lectures

Lecture 22, March 31st, 2016: Approximate Inference

In this lecture we will continue our discussion of probabilistic modelling and turn our attention to approximate inference.

Please study the following material in preparation for the class:

  • Chapter 19 of the Deep Learning Textbook on approximate inference.

In preparation for the following lecture, please study this paper as well, mentioned already in class:

 

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Lectures

Lecture 21, March 24th, 2016: RBMs and Partition Function

In this lecture we will continue our discussion of probabilistic undirected graphical models such as the Restricted Boltzmann Machine.

Please study the following material in preparation for the class:

 

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Lectures

Lecture 20, March 21st, 2016: Graphical Models

In this lecture we will begin our discussion of probabilistic undirected graphical models.

Please study the following material in preparation for the class:

  • Lecture 5 (5.1 to 5.3) of Hugo Larochelle’s course on Neural Networks.
  • Chapter 16 of the Deep Learning Textbook (important background on probabilistic models).
  • Chapter 17 of the Deep Learning Textbook (Monte-Carlo methods)

Other relevant material:

  •  Lecture 11  of Geoff Hinton’s cousera course on Neural Networks (from Hopfield nets to Boltzmann machines)
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Lectures

Lecture 19, March 17th, 2016: Representation learning

In this lecture we will step back a little on the general notion of representation learning and see how it connects with transfer learning, multi-task learning, the importance of depth, and the need for broad priors.

Please study the following material in preparation for the class:

Other relevant material:

 

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Lectures

Lecture 18, March 14th, 2016: Autoencoders

In this lecture we will continue our discuss of unsupervised learning methods. We will study autoencoders in more detail.

Please study the following material in preparation for the class:

  • Lecture 6 (6.5 to 6.7) of Hugo Larochelle’s course on Neural Networks.
  • Chapter 14 of the Deep Learning Textbook.

Other relevant material:

 

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Lectures

Lecture 17, March 10, 2016: Autoencoders

In this lecture we will begin our discuss of unsupervised learning methods. In particular, we will study a particular kind of neural network known as an autoencoder.

Please study the following material in preparation for the class:

Other relevant material:

  •  Lecture 15a of Geoff Hinton’s coursera course on Neural Networks.

 

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