Data Mining (Spring 2022)

Abstract

The course is only open in spring 2022 for students, who have been enrolled in the course previously and need to finish the course.

This course gives an introduction to the field of data mining with applications to game development.
After this course, you should be able to address complex data analyses, extracting information from large amount of data with a variety of data types.
The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining packages and individual implementation of algorithms.

Description

The course will cover the following main topics:

• The data mining process
• Data pre-processing
• Basic statistics
• Cluster analysis
  
• Classification and regression
• Deep learning

Application examples will be given from domains including demographics, image processing and games.

Formal prerequisites

Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions. This corresponds to at least having passed an introductory programming course, and preferably also an intermediate-level programming course. The course will contain compulsory programming.
The programming language used in the course is Python, which is the industry standard for data mining and machine learning.

Intended learning outcomes

After the course, the student should be able to:

• Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
• Implement basic pre-processing, association mining, classification and clustering algorithms.
• Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
• Work efficiently in groups and evaluate the algorithms on real-world problems.

Learning activities

The course includes frontal lectures and exercise sessions. The purpose of the lectures is to give an abstract perspective on the topic covered and the context around it.

During the exercise sessions, the algorithms are explained again using a more practical perspective, with hands-on examples and code descriptions.

Mandatory activities

The course has one individual mandatory hand-in is: each student is required to develop one of the algorithms presented in the first part of the course and use their implementation to perform a data analysis task on one of the data sets provided by the teacher. 

The purpose of this activity is to get hands-on experience on the inner workings of the basic concepts of machine learning upon which the second part of the class will be built.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

Jiawei Han, Micheline Kamber and Jian Pei - Data Mining: Concepts and Techniques

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 10%
• Exercises: 10%
• Assignments: 10%
• Project work, supervision included: 30%
• Exam with preparation: 20%

Ordinary exam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

Time and date