This repository will be for the two homework assignments homework 1, and homework 2 for the course 2IIG0 Data Mining & Machine Learning. Each homework assignment has their own README's if you want to learn more.
This course is an introduction to Data Mining and Machine Learning. We will cover the two main paradigms in Data Mining and Machine Learning, namely supervised and unsupervised learning. In particular, we will introduce linear and non-linear regression, classification techniques, neural networks, clustering techniques, and recommender systems. In this course, we study the underlying theoretical foundations of Data Mining and Machine Learning and apply the learned techniques to real datasets.
There are two homework to be submitted during this course. 20% of your final grade each. See the Schedule for the release dates and deadlines. The homework have to be solved and submitted in teams of 1-4 students. The grading will not be affected by the group size. That is, if you want to, then you can submit the homework all by yourself. It's going to be a lot of work, but you have all the control. In contrast, if you are in a group of students, then you have to organize your teamwork. If one of the exercises is missing because a team member didn't deliver as promised, then the missing exercise will decrease the grade of the whole team. The individual contributions of team members will be indicated via peer reviews. The grades of team members who contributed notably less will be adjusted downwards. However, grades will not be adjusted upwards for the remaining team members. If a team member is sick to the extent that they can't contribute, a solution needs to be found with the exam committee.
This is us, and our contributions:
- Tygo van den Hurk (1705709)
- Matilda Fogato (1656376)
- András Berkli (1851640)
- Abel Galambos (1846647)