PENG9560 Topics in Artificial Intelligence and Machine Learning Course description

All aids are permitted.

Basic background in statistics or probability theory. Knowledge of a programming language.

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 course is structured in five modules:

• Module 1: Unsupervised Data Mining
• Module 2: Supervised Machine Learning
• Module 3: Reinforcement Learning
• Module 4: Artificial Neural Network and Deep Learning
• Module 5: Major Concepts in Artificial Intelligence, including: complex systems (networks, cellular automata, and agent-based models) and evolutionary computing

Two internal examiners. External examiner is used periodically.

It is highly recommended that the student has taken the courses Advanced Machine Learning and Deep Learning and/or Evolutionary AI and robotics. It is also recommended to have good programming skills, such as 20ECT of previous prgramming-focused subjects.

Introduction to modern methods, techniques and tools used in projects related to the course assignment. Lectures and tutorials will be given on the tools, laboratories and facilities available at OsloMet, and their use in relation to the given assignment text, specifications, design, verification, prototyping and development. A realistic project will then be carried out where participants work together as an "applied artificial intelligence development team".

The project involves the full process from specifications, programming, testing, verification and documentation.

None.

Pass or fail.

Two internal examiners. External examiner is used periodically.