DAT405: Introduction to Data Science and AI, lp1 HT20 (7.5 hp)
This page is under construction
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.
|1||Introduction to Data Science. Getting started with Python|
|2||Regression and classification|
|4||Baysian 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.
Examiner: Marina Axelson-Fisk (firstname.lastname@example.org)
Lecturer part I: Graham Kemp (email@example.com), Computer Science and Engineering
Lecturer part II: Marina Axelson-Fisk (firstname.lastname@example.org), Mathematical Sciences
Lecturer part III: Ashkan Panahi (email@example.com), Computer Science and Engineering
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. (Links to an external site.) (Links to an external site.)
- [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. The main difference from the last course occasion is that the course is given online.
Learning objectives and syllabus
- 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 on Studieportalen: study plan.
The examination is through weekly assignments, executed in student pairs. 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, ...
Deadline for each week's assignment can be found in the assignment tab. You will have approximately 1 week from the last lecture on the topic until the deadline.
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.
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.