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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
TIN174 - Artificial intelligence
 
Syllabus adopted 2016-02-19 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING

The course is full
Teaching language: English
The course is open for exchange students
Block schedule: D

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0116 Written and oral assignments 2,5c Grading: UG   2,5c   08 Jun 2017 pm SB,  21 Aug 2017 am M
0216 Project 5,0c Grading: TH   5,0c    

In programs

TKDAT COMPUTER SCIENCE AND ENGINEERING, Year 3 (elective)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPIDE INTERACTION DESIGN AND TECHNOLOGIES, MSC PROGR, Year 1 (compulsory elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)

Examiner:

Univ lektor  Peter Ljunglöf

Replaces

TIN173   Artificial intelligence


  Go to Course Homepage

 

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Tobe eligible for the course students should have successfully completed 1 year (60 hecr) of studies within the subject Computer Science, or equivalent. Furthermore, students should have successfully completed the following courses:

•advanced programming (DIT950, TDA550, DIT260, TDA342, DAT121, or equivalent),
•data structures (DIT960, DAT036, TDA416, or equivalent),
•algorithms (DIT600, TIN092, or equivalent).

This is an advanced course: We assume academic maturity and a willingness to explore independently.The student should have the ability to complete a sizeable programming project in a programming language of her or his own choice, such as Java, Haskell, Python, Lisp, Prolog or C/C++.
It is helpful, but not mandatory, if the student has taken courses such as:

•automata theory (e.g., DIT321, TMV027),
•logic in computer science (e.g., DAT060),
•machine learning (e.g., DIT380, TDA231),
•programming language technology, (e.g., DAT151, DIT230)

Aim

Artificial Intelligence (AI) studies how computers can accomplish tasks that were traditionally thought to require human intelligence. The aim of this course is to give a deepened understanding of the possibilities and the limitations of AI methods.

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

After completing the course the student is expected to:

Knowledge and understanding
  • exemplify and describe some chosen application areas that can benefit from using AI technologies and methodologies.
  • state and describe the most important technologies and methodologies used in different subfields of AI, such as search, automatic reasoning and planning, natural language processing; including the fundamental AI algorithms in these areas and how they are used.
  • define, explain and discuss the fundamental AI algorithms in at least one of the subareas of AI, gained by a supervised group project.
  • design, program, document, and evaluate an AI-based software system so that it has acceptable performance.
Skills and abilities
  • interpret and analyze research literature, and apply it for solving AI-related problems.
  • apply the knowledge gained from this course to new problems in the AI domain.
  • write scientific reports.
Judgement and approach
  • judge what can be achieved by AI technology and what is not possible.
  • judge when it is feasible to use AI technology, and when it is not useful.
  • summarize and relate different ethical arguments in favor of or against AI.

Content

The following topics are covered by the course:

Overview of AI
  • introduction to AI
  • history, philosophical foundations and ethical issues of AI
  • application areas where AI techniques are used, such as language technology, bioinformatics, robotics, etc.
AI technologies and methodologies
  • uninformed and informed search
  • logic and deduction
  • probability theory
  • automated planning

Organisation

The course is project-oriented and divided into two parts. The smaller part is theoretical and consists of lectures and exercises that cover the most important AI topics. The larger part of the course consists of project work in groups to complete a programming project, to write one essay, and to read and comment on the work of the other groups in the course. The students form project groups, and the groups are assigned supervisors, programming projects and essay subjects.

Literature

See separate literature list.

Examination

The course is examined by an individual written examination (2.5 hec) and a programming project (5.0 hec) that is performed in groups. The AI project consists of three parts: a programming project that covers some of the AI techniques that are presented in the lectures, a written report from the programming project, and a written essay that covers a historical, philosophical or ethical issue within AI and is presented at the end of the course. The final program, the written reports, participation during supervision, and the final presentation are all important for the assessment of the project. To be accepted on the course the student must furthermore participate actively during supervising, and read and comment on the reports of the other groups in the course.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016