Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameDesign av AI-system
- CodeDAT410
- Credits7.5 Credits
- OwnerMPDSC
- Education cycleSecond-cycle
- Main field of studyComputer Science and Engineering, Software Engineering
- DepartmentCOMPUTER SCIENCE AND ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 87112
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0119 Written and oral assignments 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)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory)
- MPHPC - HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
Examiner
Ashkan Panahi- Associate Professor, Data Science and AI, Computer Science and Engineering
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
A course in programming in a general-purpose language (e.g. C/C++/Java/Python or similar). One course in mathematics (e.g. calculus, linear algebra, applied mathematical thinking), and one course in mathematical statistics. The course "Introduction to Data Science and AI" (DAT405) or similar. We strongly recommend that the student has taken a course in Machine learning, for example DAT340, TDA233 or similar, or that such a course is taken in parallel alongside this course.Aim
The purpose of the course is to explain how some different well-known AI-systems work, provide insight in how such systems are built, and practice to develop such systems. The course takes a broad perspective and includes related areas such as data science, algorithms and optimization as appropriate.Learning outcomes (after completion of the course the student should be able to)
On successful completion of the course the student will be able to:Knowledge and understanding
- Provide an overview of different applications of AI and related areas.
- Describe how some different well-known AI-systems work and how they are used.
- Explain how AI approaches relate to other kinds of advanced information processing.
- Identify problems that can be solved with AI and other advanced computational techniques.
- Design simpler Ai systems for different applications, including model choices and system design.
- Implement AI systems with programming in combination with different tools and programming libraries.
- Discuss advantages and disadvantages of different models and approaches in AI.
- Reflect over fundamental possibilities and limitations of current AI approaches.
- Critically analyze and discuss AI applications with respect to ethics, privacy and societal impact.
- Show a reflective attitude in all learning.
Content
The course teaches design of AI systems in several different ways:- Reading of papers and lectures describing different AI systems and their design (eg. AlphaZero, Watson, systems for self-driving cars, )
- Opportunities to see and try out the implementation of different simpler AI systems.
- Own problem solving in the form of design and implementation of simpler AI systems.
- Discussions about possibilities and limitations of AI, ethics and societal impact.
Organisation
Lectures and modules with exercises and mini-projects these are mainly done in groups to two persons.Literature
Reading in the form of papers etc. , to be presented as the course proceeds.Examination including compulsory elements
Assignments and mini-projects. No exam.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.