Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)

Morten Hjorth-Jensen, Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA and Department of Physics and Center for Computing in Science Education (office FØ470), University of Oslo, Norway

The teaching material is produced in various formats for running codes (jupyter notebooks) and on-screen reading. Below you will also find a link to the lecture notes as a textbook in PDF format and as a jupyter notebook as well. Projects and exercise sets are also included.

Week 34 August 21-25:Basic introduction to the course with schedule etc and start Linear Regression

HTML:
Jupyter notebook:
- ipynb file

Week 35 August 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics

HTML:
Jupyter notebook:
- ipynb file

Week 36 September 4-8: Statistical analysis and discussion of Ridge and Lasso regression

HTML:
Jupyter notebook:
- ipynb file

Week 37 September 11-15: Resampling techniques, Cross-validation and the Bootstrap

HTML:
Jupyter notebook:
- ipynb file

Week 38 September 18-22: Summary of linear regression methods and start Logistic Regression

HTML:
Jupyter notebook:
- ipynb file

Week 39 September 25-29: Logistic Regression and Gradient methods

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Jupyter notebook:
- ipynb file

Week 40 October 2-6: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

HTML:
Jupyter notebook:
- ipynb file

Week 41 October 9-13: Building a multi-layer perceptron code and introduction to Tensorflow

HTML:
Jupyter notebook:
- ipynb file

Week 42 October 16-20: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

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Jupyter notebook:
- ipynb file

Week 43 October 23-27: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks

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Jupyter notebook:
- ipynb file

Week 44 October 30- November 3: Decision Trees and Ensemble models

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Jupyter notebook:
- ipynb file

Week 45 November 6-10: Decision Trees, Random Forests and Gradient Boosting

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Jupyter notebook:
- ipynb file

Week 46 November 13-17: Support Vector Machines

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Jupyter notebook:
- ipynb file

Week 47 November 20-24: Support Vector Machines and unsupervised learning and Summary of Course with Future Perspectives

HTML:
Jupyter notebook:
- ipynb file

Textbook

As a jupyter book:
- ipynb file

Projects Fall 2023 (dates are tentative)

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