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Applied Data Analysis and Machine Learning

About the course

Course setting
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: Linear Regression and Gradient descent
Exercises week 37
Week 37: Gradient descent methods
Exercises week 38
Week 38: Statistical analysis, bias-variance tradeoff and resampling methods
Exercises week 39
Week 39: Resampling methods and logistic regression
Week 40: Gradient descent methods (continued) and start Neural networks
Week 41 Neural networks and constructing a neural network code
Exercises week 41

Projects

Project 1 on Machine Learning, deadline October 6 (midnight), 2025

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Authors

Contents

Creator
Contributors

Authors#

Creator#

Jonathan Slenders <jonathan AT slenders.be>

Contributors#

Amjith Ramanujam <amjith.r AT gmail.com>

Contents

Creator
Contributors

By Morten Hjorth-Jensen

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