Deep Learning

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This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games.

Part 1 covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. The class covers both the theory of deep learning, as well as hands-on implementation sessions in pytorch. In the homework assignments, we will develop a vision system for a racing simulator, SuperTuxKart, from scratch.

Part 2 covers a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. In the homework assignments, we develop a vision system and racing agent for a racing simulator, SuperTuxKart, from scratch.

What You Will Learn

  • About the inner workings of deep networks and computer vision models
  • How to design, train and debug deep networks in pytorch
  • How to design and understand sequence
  • How to use deep networks to control a simple sensory motor agent


  • Background
  • First Example
  • Deep Networks
  • Convolutional Networks
  • Making it Work
  • Computer Vision
  • Sequence Modeling
  • Reinforcement Learning
  • Special Topics
  • Summary

Estimated Effort

10-15 hours/week

Course Availability

  • Summer 2023

Meet Your Instructor


    Philipp Krähenbühl

    Assistant Professor

    Meet Dr. Krähenbühl

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