ACIT4510 Statistical Learning Emneplan

All aids are permitted.

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

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.

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

Professor Anis Yazidi

It is an advantage to have some experience with the following subjects:

• Mathematical Analysis
• Basic programming, such as scripting
• Statistics, specifically probability theory

• Data streaming systems
• Data mining systems and BigData platforms
• Data stream processing methods, such as, but not limited to, anomaly detection, clustering, association rule learning
• Data visualization
• Statistical analysis on large data sets
• Linear algebra applied on BigData
• Using programming to implement analysis and toolchaining

All aids are permitted.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Professor Pedro Lind