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Morten Hjorth-Jensen eee96f1e0d updating schedule
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Teaching schedule with links to material

This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.

  1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
  2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
  3. Three projects which are graded and count 1/3 each of the final grade;
  4. A selected number of weekly assignments;
  5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
  6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
  7. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
  8. Weekly emails with summary of activities will be mailed to all participants;

Weekly Schedule

For the reading assignments we use the following abbreviations:

  • GBC: Goodfellow, Bengio, and Courville, Deep Learning
  • CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
  • HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
  • AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow

Week 34 August 23-27

Week 35 August 30-September 3

Week 36 September 6-10

Week 37 September 13-17

  • Lab Wednesday: Work on Project 1

  • Lecture Thursday: Resampling methods, cross-validation and Bootstrap

  • Lecture Friday: More on Resampling methods and summary of linear regression

  • Reading recommendations:

    • Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
    • Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)
    • Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)
    • Goodfellow et al discuss some of these topics in sections 5.2-5.5.

Week 38 September 20-24

  • Lab Wednesday: Work on Project 1

  • Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories

  • Lecture Friday: Logistic Regression and gradient optimization

  • Reading recommendations:

    • See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
    • Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression
    • Hastie et al 4.1, 4.2 and 4.3 on logistic regression
    • For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.

Week 39 September 27- October 1

  • Lab Wednesday: Work on Project 1

  • Lecture Thursday: Gradient Optimization methods

  • Lecture Friday: Gradient methods and Deep Learning and Neural Networks

  • Reading recommendations:

    • See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
    • For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
    • For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4

Week 40 October 4-8

  • Lab Wednesday: Wrap up project 1 and start project 2
  • Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
  • Lecture Friday: Deep Learning and TensorFlow and Keras
  • Reading recommendations:

Week 41 October 11-15

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Deep learning and Neural Networks
  • Lecture Friday: Convolutional Neural Networks, basic elements
  • Reading recommendations:

Week 42 October 18-22

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Convolutional Neural Networks and classification problems
  • Lecture Friday: Convolutional Neural Networks and classification problems
  • Reading recommendations:

Week 43 October 25-29

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Recurrent Neural Networks
  • Lecture Friday: Recurrent Neural Networks and time series
  • Reading recommendations:

Week 44 November 1-5

Week 45 November 8-12

Week 46 November 15-19

Week 47 November 22-26

Week 48 November 29- December 2

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