Course syllabus

Jump to today

Course-PM

DAT565 / DIT407 Introduction to Data Science and AI LP1 VT24 (7.5 HP)

Course offered by the Department of Computer Science and Engineering

Contact details

Lecturer/Examiner: Dr. Rocío Mercado Oropeza <rocio.mercado@chalmers.se>
Lecturer: Dr. Bastiaan (Sebastianus Cornelis Jacobus) Bruinsma <sebastianus.bruinsma@chalmers.se>
Lecturer: Dr. Farzaneh Jalalypour <farjal@chalmers.se>
Lecturer:    Adam Breitholtz <adambre@chalmers.se>
Responsible for grading and TAs:  Firooz Shahriari Mehr <firooz@chalmers.se>

 

Doctoral students TAs:

Firooz Shahriari Mehr <firooz@chalmers.se>
Herman Bergström <hermanb@chalmers.se>
Johann Flemming Gloy <flemming.gloy@chalmers.se>
Yossra Gharbi <yossra@chalmers.se>
Muhammad Danish Waseem <muhammad.danish.waseem@gu.se>
Ricardo Muñoz Sánchez <ricardo.munoz.sanchez@gu.se>

Student TAs:

John Klint <gusjohn25@student.gu.se>
Georg Kyhn <kyhngeorg@gmail.com>
Erik Eliasson <erikelia@chalmers.se>
Wilson Lee <guslixuf@student.gu.se>
Venkata Sai Dinesh Uddagiri <uddagiri@chalmers.se>

Course Representatives:

MPDSC    
 <gusbergim@student.gu.se> Imme Lieve Bergman
TKDAT
 <jonatan.gunnarsson@hotmail.com> Jonatan Gunnarsson
MPDSC
 <nilsaxelivarsson@gmail.com> Nils Ivarsson
TKTEM
 <lapidusolle@gmail.com> Olle Lapidus
TKMED
 <kaajsa.djerfh@gmail.com> Kajsa Djerfh
TKMED
 <ahaasni0202@gmail.com> Ali Hassani

Course 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 2 laboratory sessions.

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

For an overview of all lectures, lab sessions, and responsible Teaching Assistants, please see: Front page

Lab sessions

Lab sessions will be held hybrid, with TAs available to help you both over Zoom, and in ED3354 and ED3358.

For the lab sessions on Zoom, this Wagly 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.

Course 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

Additional literature can be found in the respective modules.

Course 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 questions about how to install Python (and similar) will only be answered if there is time. There are plenty of resources for this online, please see material for weeks 1-2 of the course.

Lectures are to be held hybrid, in-person in HA4 and over Zoom. 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 to be done and submitted in groups of two students.
    • Exceptions may be requested from the Examiner in the first two weeks of the course only, but will only be granted under exceptional circumstances.
  • 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 assignment 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.
  • If you submit the assignment without meeting the above criteria and without any prior written approval for any exceptions from the Examiner, your assignment will not count as submitted and will not be graded.
  • Extensions are only given for valid reasons such as illness, serious family issues, etc. -- not for holidays or university trips.
    • To request an extension, you are required to email the Examiner BEFORE the initial deadline (midnight before assignment is due at latest).
    • Do not assume that if you request an extension, that it will be granted.
  • All assignments must use the following LaTeX format: template.
  • All assignments must be submitted as PDF files through Canvas. Read the submission instructions carefully before submitting.
  • The use of Generative AI tools (e.g., ChatGPT) in completing the assignments is strictly prohibited and is considered plagiarism in this course.

Important notes:

  • Do not distribute course materials.
    • This includes posting solutions on a public GitHub repository.
  • All assignments pass through an automated plagiarism checker in Canvas.
  • Cases of suspected plagiarism will be flagged and reported to Chalmers’ Disciplinary Committee.

Learning objectives and syllabus

Learning objectives:

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

 

Link to the course syllabi: Chalmers and GU.

See also the Study Plan.

Examination form

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

There is NO WRITTEN EXAM. Instead, the course is considered PASSED 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