Course syllabus


Welcome to the course home page for DAT405: Introduction to Data Science and AI, lp1 HT19 (7.5 hp).

The course is offered by the department of Computer Science and Engineering.

This page contains the program of the course, as well as information about the teachers, literature and examination.  A separate course PM with more detailed information, including learning outcomes, can be found here.

Course organisation

The course is divided into three parts:

  • Part I: Introduction to data science (3 weeks)
  • Part II: Statistical methods in data science and AI (2 weeks)
  • Part III: Introduction to AI (3 weeks)

Each part is in turn divided into weekly modules, with weekly assignments. The assignments are performed in student pairs, and the deadline for each assignment is Monday at noon (12:00) the following week.


Examiner: Marina Axelson-Fisk (

Lecturer part I: Graham Kemp  (, Computer Science and Engineering
Lecturer part II: Marina Axelson-Fisk (, Mathematical Sciences
Lecturer part III: Ashkan Panahi (, Computer Science and Engineering

Teaching assistant: Emilio Jorge (, Computer Science and Engineering


The schedule of the course is found in TimeEdit.


The program is preliminary. 

Week Contents Slides and reading instructions
1 Introduction to Data Science. Getting started with Python.

2019-09-02: GK-1.pdf

2019-09-03: GK-2.pdf


Anscombe's quartet: d1.txt d2.txt d3.txt d4.txt

2 Regression and classification

2019-09-09: GK-3.pdf

2019-09-10: GK-4.pdf

3 Clustering

2019-09-16: GK-5.pdf

2019-09-17: GK-6.pdf

4 Bayesian statistics and graph models

2019-09-23: MAF1.pdf
(Bi: ch2, Ba: ch1, 8-9, M: ch2.1-2.5, ch3.1-3.3, ch5.1-5.4)

2019-09-24: MAF2.pdf
(Bi: ch5, 8, Ba: ch2-4, M: ch3.5, 10.1-10.2, 19.1-19.4, 19.6)

5 Kernel methods and MCMC

2019-09-30: MAF3.pdf, MAF3.mp4
(Bi: ch6-7, Ba: ch19, M: ch14-15)

2019-10-01: MAF4.pdf, MAF4.mp4
(Bi: ch11, 13, Ba: ch23, M: ch17, 23-24)

6 Introduction to AI

2019-10-07: Lecture_1__introduction_REV2.pdf

No lecture is scheduled for Tuesday

7 Neural networks




Student Presentation 1 (all groups must be ready)

8 Search methods




Student Presentation 2 (all groups must attend )


Course literature

List all mandatory literature, including descriptions of how to access the texts (e.g. Cremona, Chalmers Library, links).

Also list reference literature, further reading, and other non-mandatory texts.

Data Science


Statistical methods for data science and AI


Student representatives

The student representatives for the course are:

Marcus Forsberg
Emma Petersson Svensson
Mattias Westerberg


Changes made since the last occasion

A summary of changes made since the last occasion.


Examination form

The examination is through weekly assignments, executed in student pairs. All assignments need to be passed in order to pass the course. Some exercises will only have a pass/fail grade, while others will be graded 3, 4, 5 (or fail). The final course grade will be an aggregate of the combined efforts. Deadline for each week's assignment will be on Monday at noon (12:00) the week after. 


Course summary:

Date Details Due