(PLEASE consider the details below as preliminary until the course begins.)

(If this page is the only page you can see, and you are currently participating in the course, you have not been properly registered. Then please send a mail to Ashkan Panahi (see below for the mail address), and we will register you!)

Course-PM / Syllabus

DAT410 / DIT728 DAT410 / DIT728 Design of AI systems lp3 VT24 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Introduction

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.

The main structure of the course is given by the weekly modules:

1. AI problem solving [Ashkan]

2. Recommendation systems [Fredrik]

3. AI tools [Lena, Fredrik]

4. Diagnostic systems [Fredrik]

5. Natural language processing [Richard]

6. Games and planning [Fredrik]

7. Dialogue systems and question answering [Ashkan]

8. Your own mini-project [Ashkan]

There may additionally be single lectures that are not connected to a particular module.

The learning objectives are found in the official course plan. The course plans for the different course codes are essentially the same.

For detailed information about the modules see the Modules page.

Contact details

Ashkan Panahi (course responsible) ashkan.panahi at chalmers.se
Fredrik Johansson (teacher in several modules) fredrik.johansson at chalmers.se
Richard Johansson (teacher in the NLP module) richard.johansson at cse.gu.se

Lena Stempfle (TA) stempfle@chalmers.se 
Christopher Kolloff (TA) kolloff@chalmers.se

Filip Kronström (TA) filipkro@chalmers.se 

Herman Bergström (TA)  hermanb@chalmers.se

 

Student Representatives

Yifan Zhao,     zhaoyif@student.chalmers.se

Mengyuan Wang,   mengyuan@student.chalmers.se

Zhiua Zhang, zhizhan@student.chalmers.se

Puthige Venugopal Chetan Acharya, chetanpv7@gmail.com

Julia Dahlberg, juldah0213@gmail.com

Elínborg Ása Ásbergsdótti, elinborgaa@gmail.com

 

contact us if you like to be a representative.

 

Schedule

The course will be in the physical format. Some material from the previous years might be provided online. 

The schedule may vary slightly from week to week, see the TimeEdit schedule and specific information for each module. Below you can find an overview of the course:

 

Examination and grading

The course is examined continuously through the module submissions. You pass the course by passing every module in the course.

The first two modules of the course are simply pass/not pass. The remaining modules are graded based on a qualitative assessment on the scale Sufficient, Good and Very Good related to the Chalmers and GU grades in the following way:

- Sufficient (3/G/40 in Canvas)
- Good (4/G/60 in Canvas)
- Very Good (5/VG/80 in Canvas)

The Canvas numbers are a way to numerically encode the qualitative grade in the Canvas grade field, and have no direct relationship to any proportion of correctly answered questions. For a normal Sufficient grade we would write 50, but the interval 40-59 is possible - the lowest possible passing level for a module is 40. As other examples, 75 indicates an upper-level "Good" and 80 indicates a borderline grade between Good and Very Good. The Canvas code 8 is used to indicate that the submission has not yet passed.

The final grade is based on a weighted sum of the graded modules. The final grade limits in Canvas are 40 (Sufficient/3/G), 60 (Good/4/G) and 80 (Very Good/5/VG).. If you are close to a grade boundary, you can discuss with the main teacher at the end of the course, and you will be given an opportunity to improve (there is no point in doing this as the course proceeds, only at the end). At GU, only final grades G and VG are available. Please note that before all assignments have passed, the final Canvas score may be misleading.

If you should not complete the course in time, and need to come back next year, it is in your best interest to keep copies of your solutions to enable future assessment.

Grading will be done by the teaching assistants (see below). If you have questions regarding the assignments, please contact the respective TAs that are responsible for that assignment directly. 

		Grading				Consultation
v	Topic	Lena	Filip	Chris	Herman	Thurs (v-1) 10:00–12:00	Mon (v) 10:00–12:00
3	AI problem solving	x	x			Lena	Lena
4	Recommendation systems	x	x			Filip	Filip
5	AI tools	x	x			Lena	Filip
6	Diagnostic systems	x			x	Herman	Lena
7	Natural language processing		x	x		Chris	Filip
8	Game playing systems			x	x	Chris	Herman
9	Dialogue systems and question answering			x	x	Herman	Chris
10	Mini project proposal	x	x	x	x	all	all
11	Mini project	x	x	x	x	---	---

Consultation sessions

Assignment consultation sessions will be held on Zoom on Thursdays 10:00–12:00 and Mondays 10:00–12:00. Please see the schedule above which TA is responsible and use the corresponding Zoom link (see below) to contact them. It may take some time for you to be admitted from the waiting room to the meeting, so please hold. Please only join the meeting during the above-mentioned hours. Also double check which TA is responsible for which consultation session! The same applies when asking questions via mail.

Lena:  https://chalmers.zoom.us/j/67296579281 

Filip:  https://chalmers.zoom.us/j/7353696169

Chris: https://chalmers.zoom.us/j/63297715262 

Herman: https://chalmers.zoom.us/j/5204619299 

No password is needed to enter the Zoom meetings, make sure you are logged in with your Chalmers/GU account, however.

Mini-projects

There will be consultation sessions only for the mini-project proposals. See below who's Zoom meeting you should join:

Chris: groups 1-20
Filip: groups 21-40
Lena: groups 41-57 (note, Lena's consultation session Monday Feb 26 will be at 13-15)
Herman: groups 58 and above

Course literature

There is no compulsory course literature. Reading instructions will be provided in connection to the modules.

Changes made since the last occasion

 The summary and reflection parts are removed from the assignments.