Course syllabus adopted 2021-02-17 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
- DepartmentMECHANICS AND MARITIME SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 55129
- Open for exchange studentsYes
- Only students with the course round in the programme overview.
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0121 Project 7.5 c Grading: TH | 7.5 c |
In programmes
- TKDES - INDUSTRIAL DESIGN ENGINEERING, Year 3 (compulsory elective)
- TKMAS - MECHANICAL ENGINEERING, Year 3 (compulsory)
Examiner
Mattias Wahde- Full Professor, Vehicle Engineering and Autonomous Systems, Mechanics and Maritime Sciences
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 use various methods for classification, machine inference, clustering, planning, and decision-making
- Describe, implement, and use basic conversational agents (human-machine dialogue)
- Describe, implement, and use neural networks and deep learning
- Describe, implement, and use reinforcement learning
- Describe, implement, and use stochastic optimization algorithms
- Analyse and critically discuss ethical aspects of AI and its applications
- Discuss and analyze various applications of AI
Content
General overview, interpretable AI vs. black-box models machine learning as a central subfield of AI, supervised vs. unsupervised approaches etc. Introduction to logic, machine reasoning, machine inference Mathematical classification, statistical classification, clustering analysis and models, clustering algorithms Classical approaches to planning and decision-making, other approaches to planning (probabilistic planning, Markov decision processes, dynamic programming). Intelligent agents, conversational AI, dialogue managers, cognitive architectures. Applications in autonomous robots and autonomous vehicles. Overview of machine learning algorithms. Introduction to neural networks and stochastic optimization algorithms, feedforward neural networks, deep learning and relevant network architectures, such as convolutional and recurrent networks. Supervised and unsupervised learning. Applications to image recognition and time series analysis. Introduction to reinforcement learning and Q-learning, with applications. Implementations that combine RL with deep learning. Evolutionary algorithms (genetic algorithms, genetic programming), particle swarm optimization, ant colony optimization, various applications Ethical aspects of automated decision-making, deceptive AI systems, interpretability and accountability, AI and the law, ethical considerations in specific applications.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
The literature will be determined later, and will consist of lecture notes and scientific papers, possibly complemented with one or two books.Examination including compulsory elements
The examination will consist of two projects and an exam at the end of the course.The results from the projects and the exam carry equal weight (50% each) in the grading.
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.