Course syllabus
Course-PM
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.
Teachers
Examiner: Marina Axelson-Fisk (marinaa@chalmers.se)
Lecturer part I: Graham Kemp (kemp@chalmers.se), Computer Science and Engineering
Lecturer part II: Marina Axelson-Fisk (marinaa@chalmers.se), Mathematical Sciences
Lecturer part III: Ashkan Panahi (ashkanp@chalmers.se), Computer Science and Engineering
Teaching assistant: Emilio Jorge (emilio.jorge@chalmers.se), Computer Science and Engineering
Program
The schedule of the course is found in TimeEdit.
Lectures
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 |
| 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 2019-09-24: MAF2.pdf |
| 5 | Kernel methods and MCMC |
2019-09-30: MAF3.pdf, MAF3.mp4 2019-10-01: MAF4.pdf, MAF4.mp4 |
| 6 | Introduction to AI |
2019-10-07: Lecture_1__introduction_REV2.pdf No lecture is scheduled for Tuesday |
| 7 | Neural networks |
2019-10-14: Lecture_2__supervised_learning_.pdf 2019-10-15: Student Presentation 1 (all groups must be ready) |
| 8 | Search methods |
2019-10-21: 2019-10-22: 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
- S.S. Skiena, The Data Science Design Manual, Springer, 2017. The Ebook is available for free from the Chalmers library from within the Chalmers network: https://link.springer.com/book/10.1007%2F978-3-319-55444-0
Python
- Jake VanderPlas. Python Data Science Handbook, O’Reilly Media, Inc., 2016. https://github.com/jakevdp/PythonDataScienceHandbook
- Jake VanderPlas. A Whirlwind Tour of Python, O’Reilly Media, Inc., 2016. https://github.com/jakevdp/WhirlwindTourOfPython
- Allen B. Downey, Think Python: How to Think Like a Computer Scientist, 2nd edition. Green Tree Press, 2015. https://greenteapress.com/wp/think-python-2e/ Interactive Edition: http://interactivepython.org/runestone/static/thinkcspy/index.html
Statistical methods for data science and AI
- [Bi]: M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- [Ba]: D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, NY, 2012.
- [M]: K. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
Student representatives
The student representatives for the course are:
| Marcus Forsberg | marfo@student.chalmers.se |
| Emma Petersson Svensson | sveemma@student.chalmers.se |
| Mattias Westerberg | mattiasweberg@gmail.com |
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 |
|---|---|---|