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Deep Learning

Course syllabus, Spring 2020



Prof. Gilles Louppe
[email protected]

???

R: reading assingment:http://web.stanford.edu/class/cs224n/project/project-proposal-instructions.pdf R: https://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html projects

R: add overview maps for each lecture, as in INFO8002 R: lec10 on RL -> restructure partly as a lecture on World Models -> nice comprehensive pedagogical example (combine mdn, rnn, vae, rl) into a big network R: lec - theory of DL -> universal approx theorem, double descent

R: projects -> use github classroom https://classroom.github.com/

R: act functions https://www.youtube.com/watch?v=bj1fh3BvqSU&feature=youtu.be

AI at ULiège

This course is part of the many other courses available at ULiège and related to AI, including:

  • INFO8006: Introduction to Artificial Intelligence
  • ELEN0062: Introduction to Machine Learning
  • INFO8010: Deep Learning $\leftarrow$ you are there
  • INFO8003: Optimal decision making for complex problems
  • INFO8004: Advanced Machine Learning
  • INFO0948: Introduction to Intelligent Robotics
  • INFO0049: Knowledge representation
  • ELEN0016: Computer vision
  • DROI8031: Introduction to the law of robots

Logistics

This course is given by:

Feel free to contact us for help!


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Lectures

  • Theoretical lectures
  • Programming tutorials
  • (No exercise sessions)

Materials

Slides are available at github.com/glouppe/info8010-deep-learning.

  • In HTML and in PDFs.
  • Posted online the day before the lesson (hopefully).

Some lessons are partially adapted from "EE-559 Deep Learning" by Francois Fleuret at EPFL.

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Textbook

None!

But we would recommend "Dive into Deep Learning" (d2l.ai) for a comprehensive and practical introduction to the field.

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Philosophy

Thorough and detailed

  • Understand the foundations and the landscape of deep learning.
  • Be able to write from scratch, debug and run (some) deep learning algorithms.

State-of-the-art

  • Introduction to materials new from research ($\leq$ 5 years old).
  • Understand some of the open questions and challenges in the field.

Practical

  • Fun and challenging course project.

Outline

  • Lecture 0: Introduction
  • Lecture 1: Fundamentals of machine learning
  • Lecture 2: Neural networks
  • Lecture 3: Convolutional neural networks
  • Lecture 4: Computer vision
  • Lecture 5: Training neural networks
  • Lecture 6: Recurrent neural networks
  • Lecture 7: Attention and transformer networks
  • Lecture 8: Auto-encoders and generative models
  • Lecture 9: Generative adversarial networks
  • Lecture 10: Uncertainty
  • Lecture 11: Theory of deep learning
  • Lecture 12: Deep reinforcement learning

Projects

Reading assignment

Read, summarize and criticize a major scientific paper in deep learning.

Pick one of the following three papers:

  • J. Redmon, A. Farhadi, "YOLO9000: Better, Faster, Stronger", 2017. [pdf]
  • A. Vaswani et al, "Attention is all you need", 2017. [pdf]
  • M. Geiger et al, "Scaling description of generalization with number of parameters in deep learning", 2019. [pdf]

Deadline: April 3, 2020 at 23:59.

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Project

Project of your choosing. Details to be announced soon.

Evaluation

  • Exam (50%)
  • Reading assignment (10%)
  • Project (40%)

The reading assignment and the project are mandatory for presenting the exam.

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Let's start!