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# Teaching schedule with links to material
# Course setting
This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.
1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
3. Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 7 for project 1, November 11 for project 2 and December 9 for project 3.
1. Two lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
2. fours hours of laboratory sessions for work on computational projects and exercises for each group.
3. Three projects which are graded and count 1/3 each of the final grade.
4. A selected number of weekly assignments;
5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
6. The course is offered as a FYS-STK4155 (Master of Science level) and a FYS-STK3155 (senior undergraduate) course;
7. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
8. Weekly emails with summary of activities will be mailed to all participants;
## Weekly Schedule
For the reading assignments we use the following abbreviations:
- GBC: Goodfellow, Bengio, and Courville, Deep Learning
- CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
- HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
- AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow