Course syllabus

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Course PM

Course name: Introduction to Artificial Intelligence

Course code: TIN175/DIT411

Credits: 7.5 (7,5 hp)

Course edition: Spring 2020 (VT20)

Course provider: Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg

Revised January 19th, 2020

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 an overview of some basic AI algorithms and an understanding of the possibilities and limitations of AI.

Content

The course gives an introduction to the subject of AI and has two main purposes.

The first purpose is to give an understanding of the different sub-areas of AI. This is done by reading literature within different AI areas, by summarizing and by discussing the literature in writing.

The second purpose is to teach basic concepts and algorithms of AI and how they can be used to solve interesting AI problems. The following topics are included:

  • soft aspects of AI, including sustainability and ethical aspects
  • supervised learning, including deep neural networks
  • unsupervised learning, including autoencoders and word embeddings
  • reinforcement learning, including deep Q-learning
  • classical search, including uninformed and informed algorithms
  • adversarial search, including algorithms for chess and backgammon
  • constraint satisfaction problems, including local search methods.

Learning objectives

On successful completion of the course the student will be able to:

Knowledge and understanding:

    • Explain basic concepts of machine learning and classical AI
    • Compare advantages and disadvantages of some basic AI algorithms
    • Account for the historical development, current situation and future prospects for some sub-area of AI.

Skills and abilities:

    • Choose appropriate algorithms for solving given AI problems in a memory- and time-efficient manner.
    • Implement efficient AI algorithms in a suitable programming language.
    • Summarize scientific progress and ethical issues.

Judgement and approach:

    • Analyze and critically discuss soft aspects of AI.
    • Summarize and constructively criticize scientific texts.

Structure

The course consists of three main sub-courses, of which two are done in groups of preferably 5 students. The groups are selected well before the group work begins.

Schedule

Here is the schedule of the course in TimeEdit. There are Lectures and Tutorials in the schedule. Tutorials are for meetings with the TAs that are usually booked in advance. Please note the following dates:

Activity Date
Project groups formed  Jan 27
topics suggested to the TAs  Jan 31
Written exam Feb 18, 8:30-12:30, Room: SB Multisal (entrance from Sven Hultins gata 8)
Mandatory exam review session Feb 19 (Lecture time/place)
Mid-Project Presentation Feb 26 (Lecture time/place)
Group submission: project and essay Mar 13
Oral group exam: project and essay Mar 17, 18 and 20 (if needed, check timeedit for tiem and place)
Written re-exam Not Scheduled

Note that the dates of the exam and the re-exam do not appear in the TimeEdit schedule.  They will be announced later.

Course material

The course material consists of texts, images, and videos. All of it is available in electronic form free of charge.

Here is a list of the course material.

Contact details

Examiner, course responsible, and teacher

Teaching assistants/supervisors

Student representatives:

Gustav Pihlquist (guspihgu@student.gu.se)

Silav Ahmed (gussilah@student.gu.se)

Sebastian Frenzel 

Course evaluation

Now that your course DIT411 is over we would really appreciate if you could fill in the course evaluation.

https://sunet.artologik.net/gu/Survey/8115 

Your feedback is very important. Please do your part by filling out evaluations for all your courses in a constructive, helpful spirit.

The survey will be open until 2020-04-07.

Best regards,

CSE Student Office

 

Communication

The main source of information about the course is the Canvas learning platform. There will be opportunities to communicate with the teachers and TAs in connection with lectures and supervision sessions through the Slack workplace. Contact the TAs for more information. You can also contact the teachers via email. Please do contact only if it is necessary and expect at least a day for the response!

Each student needs to have access to a laptop for programming purposes. Missed deadlines must be reported to the teachers one day after the deadline at the latest.

Examination

You have to pass all three sub-courses to pass the course. More information about passing the sub-courses and their grading criteria can be found on the pages describing the three sub-courses.

Grading

All sub-courses are graded U/3/4/5 for Chalmers students and U/G/VG for GU students. The final grade is decided like this:

  • GU: To get final grade VG, you need a VG grade on at least two sub-courses.

  • Chalmers: The final grade is the average of the sub-course grades, weighted by the size of the sub-course, rounded like this:

    Weighted average Final grade
    < 3.65 3
    3.65–4.50 4
    > 4.50 5

Note that the final grades on all sub-courses are individual! This means that you can get a higher or lower grade than what your other group members will get, depending on your personal contributions to the group work. To determine that we look at the your activity and knowledge during the supervision and oral exam sessions. We might also look at the commit history of the programming project.

Syllabus

This is a joint Chalmers/GU course. It has two different course codes and two different course plans, but in reality it is exactly the same course:

Comparison to the 2019 edition

The course has not undergone a major change compared to the 2019 edition. The slack platform is introduced for a more convenient communication.

Comments on the Programming project

  • A mid project presentation is introduced.

Comments on the Essay part

  • The format of the essay is simplified.
  • The topic of the essay will relate to the project. More specifically it will include
    • a rough description of your programming project
    • an overview of related work in the field
    • an analysis of possible extensions of your work 
    • a discussion of the social, ethical, economical, and societal aspects of the work (when relevant)
  • No peer reviews will be given on the essays.

Course summary:

Date Details Due