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Contents

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 35 August 23-27

• Lab Wednesday: Introduction to software and repetition of Python Programming

• Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression

• Lecture Friday: Basics of Linear Regression

• Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html

Week 36 August 30-September 3

• Lab Wednesday:

• Lecture Thursday: Linear Regression, from ordinary linear regression to Ridge and Lasso regression, linear algebra analysis, examples and discussions of codes

• Lecture Friday: Linear Regression, Linear algebra and Ridge and Lasso Regression, linear algebra analysis, examples and discussions of codes

• Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1

Week 37 September 6-10

• Lab Wednesday:

• Lecture Thursday: Statistical interpretation of Linear Regression

• Lecture Friday: Bias-Variance tradeoff

• Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2

  • Chapter

Week 38 September 13-17

• Lab Wednesday:

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

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

• Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

  • Chapter

Week 39 September 20-24

• Lab Wednesday:

• 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 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

  • Chapter

Week 40 September 27- October 1

• Lab Wednesday:

• Lecture Thursday: Gradient Optimization methods

• Lecture Friday: Deep Learning and Neural Networks

• Reading recommendations: See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

  • Chapter

Week 41 October 4-8

• Lab Wednesday:

• Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification

• Lecture Friday: Deep Learning and TensorFlow and Keras

• Reading recommendations:

  • Chapter

Week 42 October 11-15

• Lab Wednesday:

• Lecture Thursday: Deep learning and Neural Networks

• Lecture Friday: Convolutional Neural Networks, basic elements

• Reading recommendations:

  • GoodFellow et al, Chapter 9

Week 43 October 18-22

• Lab Wednesday:

• Lecture Thursday: Convolutional Neural Networks and classification problems

• Lecture Friday: Convolutional Neural Networks and classification problems

• Reading recommendations:

  • Chapter

Week 44 October 25-29

• Lab Wednesday:

• Lecture Thursday: Recurrent Neural Networks

• Lecture Friday: Recurrent Neural Networks and time series

• Reading recommendations:

  • Chapter

Week 45 November 1-5

• Lab Wednesday:

• Lecture Thursday: Decision trees, classification and regression

• Lecture Friday: Decision trees, basic algorithms

• Reading recommendations:

  • Chapter

Week 46 November 8-12

• Lab Wednesday:

• Lecture Thursday: Ensemble methods, bagging and random forests

• Lecture Friday: Boosting and gradient boosting

• Reading recommendations:

  • Chapter

Week 47 November 15-19

• Lab Wednesday:

• Lecture Thursday:

• Lecture Friday: Unsupervised Learning, k-means

• Reading recommendations:

  • Chapter

Week 48 November 22-26

• Lab Wednesday:

• Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)

• Lecture Friday: Unsupervised Learning and PCA and Clustering

• Reading recommendations:

  • Chapter

Week 49 November 29- December 2

• Lab Wednesday:

• Lecture Thursday: Unsupervised Learning and Clustering

• Lecture Friday: Summary of course

• Reading recommendations:

  • Chapter

Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway Teachers and Grading