E9:309 Advanced Deep Learning

Announcements       Syllabus       Grading       References       Slides      



When MW 3:00 - 4:30 PM
Where Microsoft Teams (Meeting Link: link)
Microsoft Teams Channel (link)
Who Sriram Ganapathy
Office C 334 (2nd Floor)
Email sriramg aT iisc doT ac doT in
Teaching Assistants Varun Krishna
Lab C 328 (2nd Floor)
Email {varunkrishna} aT iisc doT ac doT in

Announcements

  • First class on August 4, 2021 3:00 PM.
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Syllabus

  • Visual and Time Series Modeling: Semantic Models, Recurrent neural models and LSTM models, Encoder-decoder models, Attention models.
  • Representation Learning, Causality And Explainability: t-SNE visualization, Hierarchical Representation, semantic embeddings, gradient and perturbation analysis, Topics in Explainable learning, Structural causal models.
  • Unsupervised Learning: Restricted Boltzmann Machines, Variational Autoencoders, Generative Adversarial Networks.
  • New Architectures: Capsule networks, End-to-end models, Transformer Networks.
  • Applications: Applications in in NLP, Speech, Image/Video domains in all modules.
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Grading Details

3 monthly research projects from three different domains (Speech/Audio, Text, Images/Videos, Biomedical, Financial, Chemical/Physical Sciences/Mathematical Sciences) 60%
Midterm exam 10%
Final exam 30%

Pre-requisites

  • Linear Algebra
  • Random Process
  • Basic Machine Learning/Pattern Recognition course
  • Good background in Python programming.
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References

  • A significant portion of the material would come from research papers/tutorials in the domain.
  • Lecture notes in pdf format.
  • “Deep Learning”, I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016. html
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Slides

04-08-2021 Introduction. Setting the stage for the course.
 slides
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