Course syllabus for Introduction to artificial intelligence

Course syllabus adopted 2024-02-22 by Head of Programme (or corresponding).

Overview

  • Swedish nameIntroduktion till artificiell intelligens
  • CodeMMS131
  • Credits7.5 Credits
  • OwnerTKMAS
  • Education cycleFirst-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Mechanical Engineering
  • ThemeMTS 0.5 c
  • DepartmentMECHANICS AND MARITIME SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 55125
  • Open for exchange studentsYes
  • Only students with the course round in the programme overview.

Credit distribution

0121 Project 7.5 c
Grading: TH		0 c0 c0 c7.5 c0 c0 c

In programmes

Examiner

Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Programming, basic engineering mathematics.

Aim

The course aims to introduce the students to artificial intelligence including, but not limited to, the important subfield of machine learning.

Learning outcomes (after completion of the course the student should be able to)

  • Describe, implement, and apply logic and probabilistic reasoning.
  • Describe, implement, and use various methods for classification, machine inference, clustering, planning, and decision-making
  • Describe, implement, and use stochastic optimization algorithms
  • Describe and discuss conversational agents (human-machine dialogue)
  • Describe, implement, and use neural networks and deep learning
  • Describe, implement, and use reinforcement learning
  • Analyse and critically discuss ethical aspects of AI, including equality, diversity, and inclusion.
  • Discuss and analyze various applications of AI

Content

  • General overview of the vast field of Artificial Intelligence (AI)
  • Agents and logic
  • Probabilistic reasoning
  • Machine learning (classification, regression, clustering)
  • Planning, decision-making and decision support systems
  • Stochastic optimization methods (evolutionary algorithms)
  • Natural language processing and conversational agents
  • Neural networks, deep learning, and relevant network architectures, such as convolutional and recurrent networks
  • Reinforcement learning and Q-learning
  • Applications in robotics, autonomous vehicles, image recognition, time series analysis, etc.
  • Ethical aspects of AI: interpretability, accountability, bias, regulations, equality, diversity, and inclusion

Organisation

The course runs over one quarter and is organized as a series of lectures and exercise classes combined with project work. Normally, there are two lectures and one exercise class each week.

Literature

[1] Russell, S.J., Norvig, P. Artificial intelligence: A modern approach (3rd or 4th edition)
[2] Mehlig, Bernhard, Machine learning with neural networks. An introduction for scientists and engineers, (2021). 
[3] Sutton, Richard S. & Barto, Andrew G.,  Reinforcement Learning: An Introduction (2nd ed.)

Examination including compulsory elements

The examination is based on a set of assignments.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.