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  • Applied Data Analysis and Machine Learning

About the course

  • Teaching schedule with links to material
  • Teachers and Grading
  • Textbooks

Review of Statistics with Resampling Techniques and Linear Algebra

  • 1. Elements of Probability Theory and Statistical Data Analysis
  • 2. Linear Algebra, Handling of Arrays and more Python Features

From Regression to Support Vector Machines

  • 3. Linear Regression
  • 4. Ridge and Lasso Regression
  • 5. Resampling Methods
  • 6. Logistic Regression
  • 7. Optimization, the central part of any Machine Learning algortithm
  • 8. Support Vector Machines, overarching aims

Decision Trees, Ensemble Methods and Boosting

  • 9. Decision trees, overarching aims
  • 10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods

Dimensionality Reduction

  • 11. Basic ideas of the Principal Component Analysis (PCA)
  • 12. Clustering and Unsupervised Learning

Deep Learning Methods

  • 13. Neural networks
  • 14. Building a Feed Forward Neural Network
  • 15. Solving Differential Equations with Deep Learning
  • 16. Convolutional Neural Networks
  • 17. Recurrent neural networks: Overarching view

Weekly material, notes and exercises

  • Exercises week 34
  • Week 34: Introduction to the course, Logistics and Practicalities
  • Exercises week 35
  • Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
  • Exercises week 36
  • Week 36: Statistical interpretation of Linear Regression and Resampling techniques
  • Exercises week 37
  • Week 37: Statistical interpretations and Resampling Methods
  • Exercises week 38
  • Week 38: Logistic Regression and Optimization

Projects

  • Project 1 on Machine Learning, deadline October 9 (midnight), 2023
  • .md

Teaching schedule with links to material

Contents

  • Weekly Schedule

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

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Applied Data Analysis and Machine Learning

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Teachers and Grading
Contents
  • Weekly Schedule

By Morten Hjorth-Jensen

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