EPN

PENG9560 Topics in Artificial Intelligence and Machine Learning Emneplan

Engelsk emnenavn
Topics in Artificial Intelligence and Machine Learning
Studieprogram
PhD program i ingeniørvitenskap
Omfang
10 stp.
Studieår
2020/2021
Timeplan
Emnehistorikk

Innledning

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.

Anbefalte forkunnskaper

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

Læringsutbytte

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.

Innhold

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

Arbeids- og undervisningsformer

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.

Arbeidskrav og obligatoriske aktiviteter

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.

Vurdering og eksamen

Oral examination, individual.

The exam cannot be appealed.

Hjelpemidler ved eksamen

None.

Vurderingsuttrykk

Pass or fail.

Sensorordning

Two internal examiners. External examiner is used periodically.