DAT405 / DIT407 Introduction to data science and AI lp3 VT23 (7.5 hp)
Course is offered by the department of Computer Science and Engineering.
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.
|1||Introduction to Data Science. Getting started with Python|
|2||Regression and classification|
|4||Bayesian statistics and graphical models|
|5||Markov models, kernel methods and decision trees|
|6||Introduction to AI and its ethics|
|7||Machine learning and neural networks|
Zoom links to each session can be found under Home.
- Graham Kemp (week 1)
- Mohammad Kakooei (weeks 2, 3, 8)
- Vladimir Pastukhov (weeks 4, 5)
- Sebastianus (Bastiaan) Bruinsma (weeks 6, 7)
- Tobias Karlsson
- Hanna Ek
- Denitsa Saynova
- Arman Rahbar
- Newton Mwai Kinyanjui
- Simon Johansson
- Zhitao Liang
- Stefani Platakidou
- Mert Yurdakul
- Sackarias Lunman
- Daniele Murgolo
- Aditya Padmanabhan Varma
Contact details will be on the course Home page
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.
- 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:
- 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
- [Bi]: M. Bishop, Pattern Recognition and Machine Learning (Links to an external site.) , Springer, 2006.
- [Ba]: D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, NY, 2012. (Links to an external site.)
- [M]: K. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
Changes made since the last occasion
No substantial changes in content.
- 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
Link to the syllabus: Chalmers
Link to the syllabus: GU
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.
The grading scales for GU students are Pass/Fail and for Chalmers students fail, 3, 4, 5. Please read the below carefully to understand the requirements that applies to you.
Weekly assignment grades:
- Weekly assignments are only available for a specific time (see dates for each assignment).
- Weekly assignments are graded as PASS or FAIL. To pass, at least a first attempt must have been submitted before the first deadline. Should the assignment require revisions, these must be done during the time while the assignment is available.
- Extension may be allowed by prior arrangements with the examiner for valid reasons (e.g. sickness, family issues). You must then contact the examiner as soon as possible to make such an agreement. Extensions will not be granted for e.g. vacation trips.
Individual Higher Grade Quizzes:
- For Chalmers students aiming for a grade above 3, there will be three individual multiple choice quizzes.
- These should be done individually and will be challenging, covering any material from readings and/or lectures.
- To pass the course, students must pass all weekly assignments. If so, the student will be given the grade PASS (GU) or at least 3 (Chalmers).
- Chalmers students aiming for higher grades will need to in addition take the individual quizzes. Other students does not need to take the quizzes.
- It is not possible to count an individual quiz instead of a failed weekly assignment, to get a grade above 3, all weekly assignments must be passed as well.
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.