Course syllabus

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DAT565 / DIT407 -- Introduction to Data Science and AI

lp4 VT24 (7.5 HP)

Course offered by the Department of Computer Science and Engineering

 

Contact

Head Teacher and Lecturer:  Dr. Bastiaan (Sebastianus Cornelis Jacobus) Bruinsma <sebastianus.bruinsma@chalmers.se>
Lecturer/Examiner:    Dr. Moa Johansson <moa.johansson@chalmers.se>
Lecturer:    Dr. Janosch Menke <janosch@chalmers.se>
Responsible for Grading and TAs:  Dr. Mohammad Kakooei <kakooei@chalmers.se>

 

Doctoral students TAs:

Ahmet Zahid Balcioglu <ahmet.balcioglu@chalmers.se>
Filip Kronström <filipkro@chalmers.se>
Linus Aronsson <linaro@chalmers.se>
Nicolas Pietro Marie Audinet De Pieuchon <nicolas.audinet@chalmers.se>
Télio Corentin Cropsal <telio@chalmers.se>
Xuechen Liu <xuechen@chalmers.se>

Student TAs:

Venkata Sai Dinesh Uddagiri <uddagiri@chalmers.se>
Madhumitha Venkatesan <madven@chalmers.se>
Melker Rååd <melker.raad@yahoo.se>
Niphredil Klint <gusnipma@student.gu.se>
John Klint <gusjohn25@student.gu.se>

Course Representatives:

Salar Ghanbari <salarg@student.chalmers.se>
Annelie Hansson <annelieh@student.chalmers.se>
Mårten Granath <marten.gth@gmail.com>

 

 

For the lab sessions on Zoom, this https://www.waglys.com/FTkNYv is used as waiting list.

When you create a help request, first create a Zoom room and put the room id (the 10 or 11 digit number) as your name. A TA will then join the room. Do not enable a password for the room.

 

Purpose

The course gives a broad introduction to various techniques and theories used in Data Science and AI, with particular focus on their practical applications.

 

Schedule

The course includes 2 in-person lectures each week, as well as 1 laboratory session.

The schedule for lectures and sessions varies due to holidays. For the latest information, see: TimeEdit

For an Overview of all lectures, assignments and responsible Teaching Assistents, please see: Lectures and Assignments

Literature

Skiena, Steven S. (2017). The Data Science Design Manual. Springer.

Available through Chalmers Network and Library at https://link.springer.com/book/10.1007/978-3-319-55444-0 (Link opens an external website) 

Additional literature can be found in their respective modules.

 

Design

There is no mandatory attendance at either the Lectures or Laboratory sessions. However, you are unlikely to pass the course and the assignments if you do not attend.

Laboratory sessions consist of independent work on the assignments and there will be course staff available to help. The teaching assistants will primarily answer questions related to the assignment of the week, and you are assumed to have attended the lectures and done the weekly readings. General question about how to install Python etc will only be answered if there is time. There are plenty of resources for this online, see material for weeks 1-2 of the course.

Lectures are in-person only. There is required reading attached for each lecture which you are expected to read before the lecture. Lectures will assume you have read the literature and will not repeat it.

Assignments:

  • The course has 8 MANDATORY weekly assignments.
  • All assignments are done in groups of two students.
  • Assignments are released after the two relevant lectures -- not before. This way, everyone has the same amount of time to finish the assignment.
  • The initial deadline is one week AFTER initial release. Note the exceptions due to public holidays. It is your responsibility to keep yourselves informed about each individual deadline!
  • You will receive feedback and grading ONE WEEK after the initial deadline.
  • If you did not pass, you can resubmit. The resubmission deadline is TWO WEEKS after the initial deadline. After this, the assignmet is closed.
  • Resubmissions do not receive feedback, only grading.
  • Late submissions are considered resubmissions (and thus receive no feedback).
  • If you fail the resubmission, you will need to complete that assignment again in a later instance of the course (e.g. LP1).
  • All deadlines are HARD.
  • Extensions are only given for valid reasons such as illness, serious family issues etc -- not for holiday trips.
  • To request an extension, you are required to email the Examiner BEFORE the initial deadline, with the Lecturer for that week in cc. 
  • All assignments must use the following LaTeX format: Format
  • All assignments must be submitted as PDF files through Canvas.

 

Learning Objectives and Syllabus

On successful completion of the course the student will be able to:
 
Knowledge and understanding
  • describe fundamental types of problems and main approaches in data science and AI
  • give examples of data science and AI applications from different contexts 
  • give examples of how stochastic models and machine learning (ML) are applied in data science and AI
  • explain basic concepts in classical AI, and the relationship between logical and data driven, ML-based approaches within AI.
  • briefly explain the historical development of AI, what is possible today and discuss possible future development.
Skills and abilities
  • use appropriate programming libraries and techniques to implement basic transformations, visualizations and analyses of example data 
  • identify appropriate types of analysis problems for some concrete data science applications 
  • implement some types of stochastic models and apply them in data science and AI applications
  • implement and/or use AI-tools for search, planning and problem solving
  • apply simple machine learning methods implemented in a standard library
Judgement and approach
  • justify which type of statistical method is applicable for the most common types of experiments in data science applications 
  • discuss advantages and drawbacks of different types of approaches and models within data science and AI.
  • reflect on inherent limitations of data science methods and how the misuse of statistical techniques can lead to dubious conclusions 
  • critically analyze and discuss data science and AI applications with respect to ethics, privacy and societal impact
  • show a reflective attitude in all learning

 

See also the Study plan

See also the Syllabus (Chalmers)

See also the Syllabus (GU)

 

Examination

The course is graded PASS/FAIL (G/U).

There is NO WRITTEN EXAM. Instead, the course is considered PASS when one has passed all eight mandatory assignments. 

We cannot provide conversions to numeric grades or other scales for exchange students.

Course summary:

Date Details Due