Introduction to machine learning course (Practical part)

Welcome to this repository where I published my contributions to the practical part of the course INF264: Introduction to machine learning! This course is run by the University of Bergen (Norway) and I (Natacha Galmiche) was co-responsible for the practical part of the course in fall 2020 and 2022. The course is a part of the machine learning specialisation of the master program in informatics offered by the department of Informatics of the University of Bergen. This course assumes that the students have no a priori knowledge in machine learning but do have some notions of linear algebra, python and calculus. A direct follow-up of the course is INF265: Deep learning, in which I was fully responsible for the practical part. You can see the entirety of the practical part of the course "INF265: Deep Learning" on my corresponding repository.

You can find in this repository all the resources that I produced for the practical part of the course, and you may notice that some homework and exercises are missing. This is because they were done by another teaching assistant. However, homework and exercises should be rather independent from each other, which means that you can use the resources here as is. In addition to the resources you find here, the theoretical part of the course was done by the course instructor and consisted of lectures (twice a week, for a total of 16 lectures).

Course content (Practical part)

The course includes two mandatory and graded projects where students practice implementation and application of machine learning algorithms into real-world data. Project assignments are done in pairs and are available three weeks before the deadline. I did not participate to producing these projects.

In addition, there are optional weekly exercises and homework with solution to help student practice what they have learned and to prepare them for the mandatory projects. It is recommended to start with the exercises and then the homework since the exercises are usually an introduction and the homework are going further.

In this repository, you can find the following exercises and homework as well as suggestion of solution for each of them:

1. Module 1: Introduction & k-nearest neighbors
   1. Homework 1: KNN
2. Module 2: Linear models
   1. Exercise 2: Basis functions
3. Module 3: Decision trees & model selection and evaluation
   1. Homework 3: Model selection
4. Module 4: Neural networks & deep learning
   1. Exercise 4: Neural networks
   2. Homework 4: MLP
5. Module 5: Support vector machines & ensemble methods
   1. Exercise 5: Bagging and SVM
   2. Homework 5: Boosting
6. Module 6: Clustering
   1. Exercise 6: Hierarchical clustering and K-Means (Lloyd algorithm)
   2. Homework 6: Hierarchical clustering and image segmentation
7. Module 7: Dimensionality reduction
   1. Exercise 7: Dimensionality reduction
8. Module 8: Fairness & probabilistic methods
   1. Exercise 8: Imbalanced data