ACIT4510 Statistical Learning Course description

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory. The goal of this class is to provide students with the practical skillset to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

The participants are expected to know linear algebra, basic functional analysis, and basic concepts in probability theory.

No formal requirements over and above the admission requirements.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• will have a good understanding the different concepts and methods of supervised and unsupervised statistical learning and how to apply them on large data.
• has advanced knowledge of probabilistic formulation of the various learning problems

Skills

Upon successful completion of the course, the student:

• can apply different high-dimensional regression techniques on data
• can apply different classification techniques on data
• can apply clustering techniques on data
• can derive learning algorithms for new models and analyze new data with them.
• can apply dimensionality reduction techniques on data

General competence

Upon successful completion of the course, the student:

• can apply different predictive models on data and assess their performance
• can use supervised and unsupervised learning in different real life problem

This course is divided into two parts. The first part with focus on covering the principles of Statistical Learning. Different seminars will be given on the different methodological aspects of Statistical learning, mainly, supervised learning and unsupervised learning.

The second part will focus on the students completing a programming project. This is a real data analysis problem, where the student is asked to carry out the analysis using the tools and techniques from the course and hand in a report documenting the steps he has taken in the analysis. The ultimate goal is to build a predictive model.

The project report will consist of at least 25 pages and max 60 pages.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The following required coursework must be approved before the student can take the exam:

At least 80% of the lab assignments need to be approved.

50% of the grade based on the individual project (25 - 60 pages) 50% of the grade based on individual oral examination.

Both exams must be passed in order to pass the course. The oral exam cannot be appealed.

None.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Two internal examiners. External examiner is used periodically.