Kursens utveckling

The course welcomed Prof Olov Andersson on board as a new teacher. The course design was basically kept the same as in HT23 in terms of lectures (by three teachers), labs, and the format of written exams as well as the programming challenge.

For one of the recommended readings we used the 2nd edition of "An Introduction to Statistical Learning, G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor (Springer, 2023).

This course offering in P3 is meant to be basically identical to the one in P1, hence no significant updates were made.

The course was held back on campus (IRL) and so was the exam accordingly. For this reason the exam on campus was also organised as normal, not as an open-book exam which we introduced during the pandemic.

This offering in P3 is meant to be basically the same as the one given in P1.

Improved Lab 2 instructions, e.g. added a paragraph with hints on how to debug by using a minimal training dataset (the outcome and intermediate variables can be hand-calculated). Used a feedback questionnaire on the part of probabilistic machine learning (results in the course analysis report). Increased the chances and hours for the drop-in sessions.

This offering in P3 is meant to be basically the same as the one given in P1. Instructions in Lab 2 augmented with Python hints. An additional reference material describing Monty Hall Problem put on Canvas (used to be introduced as the Three Prisoners Problem during the lecture). Lab-booking system revised.

New course code given while the credits increased by 1.5 credits. Numerous additions across different topics (details in the course analysis). Final grade based on the exam A-E, with mandatory laboratory work.