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.

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