Plans for the weeks March 20-31 and rest of semester

Dear all, I hope you are all doing well.
This week we will wrap up our discussion of autoencoders (AEs) and how these can be used in connection with the project. We will also link the discussion of AEs to the principal component analysis and finalize the discussion of the PCA method as well.  The lecture slides (missing some codes which will be added later today) are at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week9/ipynb/week9.ipynb

Else, we will continue with lectures throughout the semester and will focus on unsupervised methods and so-called generative models. The plans (tentative) for the rest of the semester are  listed below here.
Best wishes to you all,
Morten

## March 20-24
- Autoencoders and discussions of codes and links with PCA
  - Reading recommendation: Goodfellow et al chapters 14
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesMar222023.pdf

## March 27-31
- Deep generative models
  - Monte Carlo methods and structured probabilistic models for deep learning
  - Partition function and Boltzmann machines
  - Reading recommendation: Goodfellow et al chapters 16-18
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesMar292023.pdf

## April 10-14

- Deep generative models and Boltzmann machines
  - Reading recommendation: Goodfellow et al chapter 20.1-20.7
- Project work
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesApr122023.pdf

## April 17-21
- Deep generative models and Boltzmann machines
  - Generative Adversarial Networks (GANs)
  - Reading recommendation: Goodfellow et al chapter 20.10-20.14
- Project work
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesApr192023.pdf

# April 24-28
- Gaussian processes and Bayesian statitics
- Project work
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesApr262023.pdf

# May 1-5
- Gaussian processes and Bayesian statistics
- Project work
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesMay32023.pdf

# May 8-12
- Gaussian processes and Bayesian statistics and last session
- Project work
- Video of lecture at https://youtu.be/
- Handwritten notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/NotesMay102023.pdf

## Recommended textbooks:

o Goodfellow, Bengio and Courville, Deep Learning at https://www.deeplearningbook.org/

o Brunton and Kutz, Data driven Science and Engineering at https://www.cambridge.org/highereducation/books/data-driven-science-and-engineering/6F9A730B7A9A9F43F68CF21A24BEC339#overview