lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
44de6189d3681999633d0699378a5556f54dea9b
FYS-STK4155/doc/LectureNotes/schedule.md
T
Morten Hjorth-Jensen 44de6189d3 lecture schedule
2022-09-22 15:15:27 +02:00

10 KiB
Raw Blame History

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. The deadlines for the projects are October 7 for project 1, November 11 for project 2 and December 9 for project 3.
  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-STK4155 (Master of Science level) and a FYS-STK3155 (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 22-26

Week 35 August 29-September 2

Week 36 September 5-9

Week 37 September 12-16

  • Lab Wednesday: Work on Project 1
  • Lecture Thursday: Resampling methods, cross-validation and Bootstrap
    • Thursday September: Summary of Ridge and Lasso with examples and start resampling techniques
  • 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 19-23

  • Lab Wednesday: Work on Project 1
  • Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories and gradient optmization
  • Lecture Friday: Logistic Regression and gradient optimization
    • Video of Lecture at
  • 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 26-30

  • Lab Wednesday: Work on Project 1
  • Lecture Thursday: Gradient Optimization methods
    • Video of Lecture at
  • Lecture Friday: Gradient methods
    • Video of Lecture at
  • Reading recommendations:

Week 40 October 3-7

  • Lab Wednesday: Wrap up project 1
  • Lecture Thursday: Stochastic gradient descent, automatic differentiation and start discussion of feed-forward Neural Network code for regression and classification
    • Video of Lecture at
  • Lecture Friday: Deep Learning and Neural Networks: the back propagation algorithm
    • Video of Lecture at
  • Reading recommendations:

Week 41 October 10-14

Week 42 October 17-21

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
    • Video of Lecture at
  • Lecture Friday: Convolutional Neural Networks and classification problems
    • Video of Lecture at
  • Reading recommendations:

Week 43 October 24-28

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Recurrent Neural Networks
    • Video of Lecture at
  • Lecture Friday: Recurrent Neural Networks and time series and principal component analysis (PCA)
    • Video of Lecture at
  • Reading recommendations:

Week 44 October 31-November 4

  • Lab Wednesday: Work on project 2
  • Lecture Thursday: Summary on PCA and discussion of Clustering for unsupervised learning. Decision trees, classification and regression
    • Video of Lecture at
  • Lecture Friday: Decision trees, basic algorithms
    • Video of Lecture at
  • Reading recommendations:

Week 45 November 7-11

  • Lab Wednesday: Work on project 2, project 3 available.
  • Lecture Thursday: Ensemble methods, bagging and random forests
    • Video of Lecture at
  • Lecture Friday: Boosting and gradient boosting
    • Video of Lecture at
  • Reading recommendations:

Week 46 November 14-18

Week 47 November 21-25

  • Lab Wednesday: Work on project 3
  • Lecture Thursday: Support Vector Machines
    • Video of Lecture
  • Lecture Friday: Support Vector Machines and Summary of course
    • Video of Lecture
  • Reading recommendations:
Reference in New Issue View Git Blame Copy Permalink