Course description forPENG9560 Topics in Artificial Intelligence and Machine Learning

Introduction

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.

Recommended preliminary courses

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

Learning outcomes

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.

Content

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

Teaching and learning methods

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.

Course Requirements

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.

Assessment

Oral examination, individual.

The exam cannot be appealed.

Permitted Exam Materials and Equipment

None.

Grading scale

Pass or fail.

Examiners

Two internal examiners. External examiner is used periodically.

Course information

Course name in Norwegian
Topics in Artificial Intelligence and Machine Learning
Study programme
Fall: PhD program i ingeniørvitenskap
Weight
10 ECTS
Year of study
2019
Curriculum
SPRING 2020
Schedule
SPRING 2020
Programme description
Fall 2019: PhD program i ingeniørvitenskap
Subject History