Plans for the week of March 11-15

Dear all, welcome back to a new week! We obviously hope that you've passed a great weekend.

This week we will discuss autoencoders (AEs) and this will be our final topic before the Easter break. The motivation behind the discussion of AEs is that they will allow us to catch at least two birds with one stone;

1) link with the principal component analysis and dimensionality reduction methods in general

2) prepare the ground for generative models, which will be the main topic after the Easter break.

We will dedicate the lecture on this coming Tuesday (March 12) and March 19 to the analysis of AEs. 

The more detailed plans for this week are

• Discussion of Autoencoders (AEs)
• Links between Principal Component Analysis (PCA) and AE. The lecture material has a larger focus on the PCA, which is an important unsupervised method as well as an important dimensionality reduction method

Reading recommendations:

• Goodfellow et al chapter 14.
• Rashcka et al. Their chapter 17 contains a brief introduction only.
• Deep Learning Tutorial on AEs from Stanford University at http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/
• Building AEs in Keras at https://blog.keras.io/building-autoencoders-in-keras.html
• Introduction to AEs in TensorFlow at https://www.tensorflow.org/tutorials/generative/autoencoder

The lecture slides are at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week9/ipynb/week9.ipynb

Else, we have a regular lab on Thursdays at 215pm-4pm, room FØ397.

Best wishes to you all,

Keran, Morten and Ruben