Course syllabus

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DAT405 / DIT406 Introduction to data science and AI lp4 VT22 (7.5 hp)

Course is offered by the department of Computer Science and Engineering.

 

The course will be offered in both formats on-campus and online. Lectures will be streamed live via zoom, so we cannot guarantee that the video and sound quality will be perfect (have to make do with available equipment).

To connect to the (online) lectures via zoom, please use the following link: 

https://chalmers.zoom.us/j/64917869545      Password: 390385

 

Lab sessions will also be hybrid.

To get help remotely from TAs during the lab-sessions:

  • Students create zoom meeting (don't set any password for the meeting), and go to the course account at Waglys and click “Request help” button. Students then put the meeting ID in the textbox “Your name”.
  • Each TA checks the queue system, takes/removes the first meeting ID on the queue and connects to that meeting ID. Other TAs will take the first in the updated queue.
  • If while waiting, students have found the solution themselves and don't need from the TAs, the students must remove their meeting ID from the queue.
  • Responsible TA should clear the queue system just before the end of each lab session.
  • The lab rooms are booked for the course and you may use these  rooms and the TAs will be there to help you. 

Here is the course account at Waglys: https://www.waglys.com/7hAo7g#

 

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 Tuesday (23:59) the following week (with some slight exceptions for the weeks around easter - detailed information coming). Students must organise themselves into groups in the first week, please use the Discussion Forum in Canvas to search for a partner. Indicate your ambition-level (grade 3, 4, 5) so that groups can be more easily evenly matched.

If there are any problems (e.g. partner dropping course), please contact the course teachers as soon as possible, so we can resolve the situation.

Week Topic Lecturer TA:s (head TA in bold)
1 Introduction to Data Science. Getting started with Python Marwa Naili Bastiaan Bruinsma, Bharath Poojary
2 Regression and classification Marwa Naili Bharath Poojary, Divya Grover
3 Clustering Moa Johansson Tobias Karlsson, Bharat Pojary
4 Bayesian statistics and graphical models Moa Johansson Tobias Karlsson, Bastiaan Bruinsma
5 Markov models, kernel methods and decision trees Moa Johansson Divya Grover, Tobias Karlsson
6 Introduction to AI and its ethics Bastiaan Bruinsma Bastiaan Bruinsmaa
7 Machine learning and neural networks Marwa Naili Divya Grover, Ricardo Muñoz Sánchez, Tobias Karlsson
8 Rule-based AI Moa Johansson Ricardo Muñoz Sánchez

 

Contact details

Examiner/Lecturer:  Moa Johansson (moa.johasson@chalmers.se)

Lecturers: 

Teaching assistants:

Student representatives:

Schedule

Please view the up-to date schedule directly in TimeEdit. Lectures are normally scheduled on Tuesdays and Wednesdays at 8.00 - 9.45 in room HC3. Lab sessions are on Wednesdays at 13.15 - 15.00 and Fridays at 10.00 - 11.45 in room ED2480.

There are some important exceptions to the above:

  • The first lecture on Tuesday 22/3 is in room HC2.
  • Week 15 (11-14 April): no lectures or labs due to Easter, re-exams etc.
  • Week 16 (18-22 April): Changed schedule due to easter. Lectures on 8.00 on Wednesday 20/4 and Thursday 21/4. Lab sessions on Thursday 21/4 at 10.00 and Friday 22/4 at 10.

Course literature

The course has no mandatory textbooks, but the following books are recommended for consultation. The page for each module contains links to lecture slides and other supplementary material, including some links to video material.

Data Science

Python

Statistical methods for data science and AI

Free online collaboration tools

For the assignments you will be required to submit a PDF file and/or a Jupyter notebook. Here are some suggestions for online collaboration tools.

Make sure you export your work to either PDF or a Jupyter notebook before you hand them in!

Changes made since the last occasion

N/A

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

Link to the syllabus on Studieportalen.

Study plan

If the course is a joint course (Chalmers and Göteborgs Universitet) you should link to both syllabus (Chalmers and Göteborgs Universitet).

Assignments

The examination is through weekly assignments, carried out in student pairs. Always submit only your own work, no copying of text or code is allowed. We will use automated plagiarism checkers.

The deadline for each week's assignment can be found at the foot of this page. You will have approximately 1 week from the last lecture on the topic until the deadline. After the submission deadline, TA:s will mark the assignment within 10 days, i.e. by the Friday the following week.

Grades

  • Assignment grades
    • Each assignment will be graded with 1-10 points
    • Special rules apply for grades on late submissions without a valid reason (contact head TA for relevant assignment, see above):
      • late <= 1 day (24h), can get max 9 points.
      • 1 < late <= 2 days, can get max 8 points
      • 2 < late <= 3 days, can get max 7 points
      • 3 < late <= 4 days, can get max 6 points
      • late > 4 days, can get max 5 points
  • Course grades:
    • Chalmers grades: Fail, 3, 4, 5
    • The course grade will be based on the sum of the assignment grades
    • At least 5 points on all Assignments are needed for a passing grade
      • If the score is lower than 5 points you will have to resubmit, but the score on the resubmission cannot be higher than 5 points.
    • No resubmissions are allowed unless under the above criteria.
    • Point needed for final grade (must also pass all assignments):
      • Chalmers:
        • 5: >= 68 (85%)
        • 4: >= 56 (70%)
        • 3: >=40  (50%)

Examination form

There will be 8 assignments.

Grading will be based on a qualitative assessment of each assignment. It is important to:

  • Present clear arguments
  • Present the results in a pedagogical way
    • Should it be table/plot? What kind of plot? Is everything clear and easy to understand?
  • Show understanding of the topics
  • Give correct solutions.
  • Make sure that the code is well commented.
    • Important parts of the code should be included in the running text and the full code uploaded to Canvas.

To obtain grade 3 or higher you must pass all the assignments.

The final grade  is based on an overall assessment at the end of the course.

Course summary:

Date Details Due