Course syllabus adopted 2023-02-07 by Head of Programme (or corresponding).
Overview
- Swedish nameIntelligenta agenter
- CodeTME286
- Credits7.5 Credits
- OwnerMPCAS
- Education cycleSecond-cycle
- Main field of studyEngineering Physics
- DepartmentMECHANICS AND MARITIME SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 11117
- Maximum participants80 (at least 10% of the seats are reserved for exchange students)
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0121 Project 7.5 c Grading: TH | 0 c | 0 c | 7.5 c | 0 c | 0 c | 0 c |
In programmes
- MPALG - COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (elective)
- MPCAS - COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
- MPHPC - HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
- MPSYS - SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (elective)
Examiner
Mattias Wahde- Full Professor, Vehicle Engineering and Autonomous Systems, Mechanics and Maritime Sciences
Eligibility
General entry requirements for Master's level (second 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
English 6 (or by other approved means with the equivalent proficiency level)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
Engineering mathematics and (preferably object-oriented) programming.Aim
The aim of the course is for the students to gain knowledge regarding human-machine interaction, in general, and intelligent (software) agents, in particular. Such systems are relevant in many different fields, such as driving assistance, health and elderly care, finance etc.Learning outcomes (after completion of the course the student should be able to)
- understand and implement basic text processing
- Implement and compare different methods for text classification
- understand and discuss statistical language models.
- define and implement (in C#. NET och Python) so-called chatbots
- define and impement (in C#. NET) so-called task-oriented agents, with particular focus on dialogue management
- analyze and compare interpretable models and blackbox models.
- describe and apply speech synthesis and speech recognition in intelligent agents
- describe and compare various different applications of intelligent agents
- discuss various ethical implications of intelligent agents
Content
- Statistical language models and text processing
- Text classification
- Programming in C# .NET for intelligent agents (throughout the course)
- Some Python programming
- Chatbots
- Speech and voice recognition (briefly)
- Human-machine dialogue
- Applications (various)
- Ethical aspects of intelligent agents
Organisation
The course runs over one quarter and is organized as a series of lectures combined with assignments. The assignments are carried out individually by each student.Literature
A compendium and a selection of scientific papers.Examination including compulsory elements
The examination is based on a set of assignments, solved individually by each student.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.