Data Mining Techniques

Course Objective

The aim of the course is that students acquire data mining knowledge and skills that they can apply in a business environment. More precisely, the following learning goals are distinguished:

1. Understand the data mining process

2. Understand exploratory data analysis

3. Understand feature engineering

4. Understand classification techniques

5. Understand regression techniques

6. Understand association rules and recommender systems

7. Understand big data engineering and big data infrastructures

8. Understand current state of the art in data mining

9. Understand ethical concerns and evaluation of results

10. Learning to argue for choices (rationale)

The course if focused on the Dublin descriptors Applying Knowledge and Understanding (since the aim is to improve the practical skills); Making judgements (which technique is appropriate, how to best apply Data Mining for a specific case); Communication skills (how to report on your approach, choices and your results), and Learning skills (able to find

new relevant techniques, assess their suitability, etc.).

How the aims are to be achieved: Students will acquire knowledge and skills mainly through the following: an overview of the most common data mining algorithms and techniques (in lectures), a survey of typical and interesting data mining applications, and practical assignments to gain "hands on" experience. The application of skills in a business environment will be simulated through various assignments of the course.

Course Content

The course is intended to introduce Data Mining Techniques to students that are new to the field as well as to more experienced students. The main aim is to gain a more practical perspective towards Data Mining Techniques/Machine Learning. Lectures will cover more basic things for those new to the field (general introduction into Data Mining, classical

algorithms such as decision trees, association rules, neural networks, ensemble learning, etc.) and on top will discuss advanced topics including deep learning, recommender systems, big data infrastructures, and text mining. A number of successful applications in the area will also be discussed. In addition to lectures, there will be an extensive practical part, where students will experiment with various data mining algorithms and data sets. The grade for the course will be based on

these practical assignments.

Additional Information Teaching Methods

Lectures (h) and practical sessions (pra). Lectures are planned to be
interactive: there will be small questions, etc.

Method of Assessment

Practical assignments. There will be two assignments done in groups of three. For the first assignment there is a

choice: going for a basic version of the assignment (suited for those new to the domain) or a more advanced one (for students with more experience). The second assignment is the same for all and will involve an in-class Kaggle competition. The first assignment counts for 40% and the second for 60%.

A check will be performed on an individual student level to make sure that (1) each student understands the content of the graded material, and (2) has made a sufficient contribution to the group work. Only when this check is passed will a final grade be given. Furthermore, the grade of both assignments needs to be sufficient to pass the course. Finally, attendance of the final lecture (in which the final ranking of the Kaggle competition is announced and selected groups of students will be asked to present their solution) is mandatory as it contributes to the learning objectives of the course.

There is an option to resit one of the assignments when the assignment has been submitted but an insufficient grade is obtained (only when the other assignment grade is sufficient). This involves improving your assignment to at least a sufficient level within two weeks after having received your grade for the second assignment. The maximum grade of the resit is a 6.0.

Literature

Optional: Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J.
Pal. Data Mining: Practical Machine Learning Tools and Techniques
(Fourth Edition). Morgan Kaufmann, 2016 978-0128042915.

Additional Information Target Audience

Master Artificial Intelligence, Master Computer Science, Master

Bioinformatics and Systems Biology, Master Business Analytics

Recommended background knowledge

1. Statistics
2. Programming Skills