Program Overview

AAU Modules

Machine Learning

2022/2023

Content, Progress, and Pedagogy of the Module

Learning Objectives

Knowledge

• Key models in machine learning and their associated learning and inference techniques, such as:
  • Statistical linear models
  • Markov chains and hidden Markov models
  • Support Vector machines
  • Neural Net
  • Probabilistic Graphic Models
  • Matrix factorization
• The use of machine learning methods in selected fields of application, such as:
  • Web and network mining
  • Recommendation Systems
  • Computer games
  • Image analysis
  • Text mining

Skills

• Be able to apply advanced techniques from machine learning to the construction of intelligent systems

Competences

• To understand advanced machine learning methods for designing intelligent systems
• To analyze their usefulness and impact in solving specific tasks

Type of Instruction

The type of instruction is organised in accordance with the general instruction methods of the programme.

Extent and Expected Workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

• Name of exam: Machine Learning
• Type of exam: Written or oral exam
• ECTS: 5
• Assessment: 7-point grading scale
• Type of grading: Internal examination
• Criteria of assessment: The criteria of assessment are stated in the Examination Policies and Procedures.

Additional Information

• Contact: The Study board for Computer Science.

Facts About the Module

• Danish title: Maskinlæring
• Module code: DSNCSITK225
• Module type: Course
• Duration: 1 semester
• Semester: Spring
• ECTS: 5
• Language of instruction: English
• Empty-place Scheme: Yes
• Location of the lecture: Campus Aalborg
• Responsible for the module: Lone Leth Thomsen

Organisation

• Study Board: Study Board of Computer Science
• Department: Department of Computer Science
• Faculty: The Technical Faculty of IT and Design