DM859: Applied Machine Learning (5 ECTS)

STADS: 15019401

Level
Master's level course

Teaching period
The course is offered in the spring semester.

Teacher responsible
Email: zimek@imada.sdu.dk

Timetable
Show entire timetable
Show personal time table for this course.

Comment:
Ubegrænset deltagerantal. Fælles undervisning med DM855.

Prerequisites:
Entry requirement is a BA-degree in computer science.

Academic preconditions:

• Programming knowledge and basic understanding of probability and mathematics is expected.
• The course is co-taught with DM855. The course cannot be chosen by students, who have either followed, or has passed DM855.

Course introduction
The aim of the course is the introduction of data mining and machine learning techniques. The course focuses on a broad spectrum of advanced techniques for data mining on large and versatile data sets. Example application area of the presented techniques are ranging from biomedical applications to various kinds of financial, social, commercial, and scientific fields. Broadly speaking, the methods enable computational systems to identify meaningful patterns in the data and to adaptively improve their performance with experience accumulated from the observed data. The course introduces state-of-the-art advanced machine learning techniques in a practical, application-driven fashion. The students have the opportunity to experiment and to apply the introduced techniques to selected problems.

 

Application areas are all kind of data, such as financial, social, commercial, and scientific data.

The course gives an academic basis for writing a Master's thesis in topics involving machine learning and data mining.

In relation to the learning outcomes of the degree the course has explicit focus on:

• giving the competence to plan and execute complex machine learning tasks
• knowledge of common data mining tasks and methods
• application of common data mining methods to real world problems
• developing skills in the design of new variants of data mining methods
• programming and using computer tools for data mining tasks
• general experimental design in the context of statistical and computational data analysis
• interpretation of experimental data using computational methods

Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:

• describe the data mining tasks presented during the course;
• describe the algorithms and methods presented in the course;
• describe the topics presented in the course in precise mathematical language;
• explain the individual steps of mathematical derivations presented in class;
• apply the methods to simple problems;
• apply the methods to situations different from the ones presented in class;
• reflect on and assess design choices for data mining and statistical learning systems;
• undertake experimental evaluations of data mining methods and report the results.

Subject overview
The following main topics are contained in the course:

• data preparation and cleaning (for example, feature selection, outlier detection)
• specific data mining techniques, such as
• random forests,
• neural networks,
• support vector machines, and
• regression, their intuition and application
• ensemble learning

Literature

Meddeles ved kursets start.

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
None

Assessment and marking:


Expected working hours
The teaching method is based on three phase model.
Intro phase: 24 hours
Skills training phase: 12 hours, hereof:
 - Tutorials: 12 hours

Educational activities

• reading from textbooks
• solving homeworks
• applying acquired knowledge in practical projects

Educational form
In the intro phase, concepts, theories and models are introduced and put into perspective. In the training phase, students train their skills through exercises and dig deeper into the subject matter. In the study phase, students gain academic, personal and social experiences that consolidate and further develop their scientific proficiency. Focus is on immersion, understanding, and development of collaborative skills.

Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.

Remarks
Students who have passed DM205 can not follow this course.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.