Course name: Introduction to data science and AI
Course code: DAT405 / DIT405
Period: Study Period 3, 2021
The course is offered by the department of Computer Science and Engineering at Chalmers and University of Gothenburg.
Note that the lab sessions with supervision differ from those in TimeEdit, see the section on lab sessions in Home for more details.
Zoom links to each session can be found under Home.
The course is divided into eight modules, with one assignment per module. The assignments are performed in student pairs.
|2||Regression and classification|
All lectures will be recorded and uploaded to Modules on Canvas. Students can choose if they want to have their video camera and microphone turned on or off. They can also choose to watch the lectures live (recommended), or offline afterwards.
All lecture slides will also be uploaded to Modules after the lectures.
For reasons of privacy and copyright, we ask everyone not to distribute videos, lecture notes, or other course material!
The examination is through assignments, executed in student pairs (not one or three or four). If some people are in groups of their own we might join them together. If you have trouble finding a partner to do the assignments with, please make a post in the Discussions tab so you and the other(s) in a similar situation can contact each other.
Deadline for each module's assignment and the responsible TA can be found in the Assignments tab. We aim to release the scores one week after the deadline but there might be some days delay on some assignment.
Changes made since the last occasion
Updated/revised/changed assignments. No other substantial changes in modules' content.
The course gives a broad introduction to various techniques and theories used in Data Science and Artificial Intelligence (AI), with particular focus on their practical applications.
Learning objectives and syllabus
Learning objectives. On successful completion of the course the student will be able to:
- 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.
- 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
- 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
Syllabus. Link to the syllabus on Studieportalen:
The course is examined through 8 assignments. 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.
- Assignment grades
- Each assignment will be graded with 1-10 points
- Special rules apply for grades on late submissions without valid reason (contact Emilio):
- 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
- GU grades: U (Fail), G (Pass), VG (Pass with Distiction)
- 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 allowed unless under above criteria.
- Point needed for final grade (must also pass all assignments):
- 5: >= 68
- 4: >= 56
- 3: >=40
- VG: >= 64
- G: >= 40
Under the Modules tab there are links to lecture slides, lecture recordings, and supplementary material. The lecture slides contain links to videos, blog posts, and more. There are no required textbooks for this course, but the following books can be consulted.
- S.S. Skiena, The Data Science Design Manual, Springer, 2017. The Ebook (Links to an external site.) is available for free from the Chalmers library from within the Chalmers network and GU library.
- Jake VanderPlas. Python Data Science Handbook (Links to an external site.), O’Reilly Media, Inc., 2016. (Links to an external site.)
- Jake VanderPlas. A Whirlwind Tour of Python (Links to an external site.), O’Reilly Media, Inc., 2016.
- Allen B. Downey, Think Python: (Links to an external site.) How to Think Like a Computer Scientist (Links to an external site.), 2nd edition. Green Tree Press, 2015. (Links to an external site.) (Links to an external site.)
Statistical methods for data science and AI
- M. Bishop, Pattern Recognition and Machine Learning (Links to an external site.), Springer, 2006. (Links to an external site.) (Links to an external site.)
- D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, NY, 2012. (Links to an external site.)
- K. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
To contact the teaching team, please follow the instructions under the heading Contact below!
Claes Strannegård: examiner, course responsible, lecturer for modules 1, 2, 3, 4, 5, 7, 8
Simon Olsson: lecturer for module 6
Emilio Jorge (email@example.com)
Divya Grover (firstname.lastname@example.org)
Arman Rahbar (email@example.com)
David Bosch (firstname.lastname@example.org)
Anton Johansson (email@example.com)
Denitsa Saynova (firstname.lastname@example.org)
Azadeh Karimisefat (email@example.com)
Erik Gunnarsson (firstname.lastname@example.org)
Kaver Hui (email@example.com)
Panagiotis Moraitis (firstname.lastname@example.org)
Adnan Fazlinovic (email@example.com)
The student representatives of the course are:
If you want to contact the teaching team, please follow these instructions:
- Questions about the course
- Look for answers on the Canvas pages
- Use the Canvas Discussion forum
- Questions about the lectures
- During lectures: Use the chat room in Zoom
- Other times: Use the Canvas Discussion forum
- Questions about the assignments
- During lab sessions: Use the Waglys system to contact a TA
- Other times: Use the Canvas Discussion forum
- If you can’t post it, contact the responsible TA directly. Check the Assignments page to see who is responsible.
- Need to change groups?
- Contact Emilio Jorge
The syllabus page shows a table-oriented view of course schedule and basics of course grading. You can add any other comments, notes or thoughts you have about the course structure, course policies or anything else.
To add some comments, click the 'Edit' link at the top.