PENG9560 Topics in Artificial Intelligence and Machine Learning Course description

This course covers advanced topics in artificial intelligence and machine learning, both theory and practice, recent scientific papers and state-of-the-art techniques.The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

Se mer utfyllende informasjon om praksis i programplanen og i praksisguiden for PPU-Y

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student has:

• an in-depth understanding of machine learning in its main forms: supervised, unsupervised, and reinforcement learning, both theoretical and applied, to solve real- lifeproblems.
• knowledge and understanding of the main concepts of deep learning.
• knowledge and understanding of some major concepts in artificial intelligence, including: complex systems (network models, cellular automata, and agent-based models) and evolutionary computing.

Skills

On successful completion of the course, the student can:

• apply techniques from machine learning to real-life problems.
• analyse data sets with the aid of machine learning algorithms.

General competence

On successful completion of the course, the student can:

• use libraries for programming deep learning algorithms such as TensorFlow.
• deploy models to relevant real-life problems.
• solve computational problem using evolutionary computing.

Each module will be taught in a series of lectures. At the end of each module, the students will be assigned a small project to be submitted within a given deadline.

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

Compulsory assignments must be approved prior to the exam. The students must submit a small project at the end of each module. All five projects must be approved before examination.

Se mer utfyllende informasjon om praksis i programplanen og i praksisguiden for PPU-Y

Se mer utfyllende informasjon om praksis i programplanen og i praksisguiden for PPU-Y

Det er et krav om 100% tilstedeværelse i praksis i alle praksisperiodene. Ved fravær må dagene tas igjen etter avtale med praksissted og emneansvarlig/praksisseksjonen ved LUI.

Se mer utfyllende informasjon om praksis i programplanen og i praksisguiden for PPU-Y