Content: | Graduate course on Machine Learning and application/project in science (7.5 ECTS). |
Level: | Intended for students at graduate level (4th--5th year) and new Ph.D. students. |
Prerequisites: | Math (calculus and linear algebra) and programming experience (preferably Python). |
When: | Mondays 13-17 and Wednesdays 9-17 (Week Schedule Group C) in Block 4 (25/04-24/06 2022). |
Where (lectures): | Mondays: Lille UP1 at DIKU (except weeks 19-21, then Aud3 at HCO), Wednesdays: Aud1 at HCO. |
Where (exercises): | Mondays: Kursussal 1, 3, and 4A at Zoological Museum. |
Wednesdays: DIKU rooms 1-0-26, 1-0-30, and bib 4-0-17 (mornings) and Aud1 at HCO (afternoons), see KU Room Schedule plan. | |
Format: | Shorter lectures followed by computer exercises and discussion with emphasis on experience and projects. |
Text book: | References to Elements of Statistical Learning II. |
Suppl. literature: | We (i.e. you) will make extensive use of online ML resources, collected for this course. |
Programming: | Primarily Python 3.8+ with a few packages on top, though this is an individual choice. |
Code repository: | All code we provide can be found in the AppliedML2022 GitHub respository. |
Communication: | All announcements will be given through Absalon. To reach me, Email is preferable. |
Collaborative tools: | For "short coding communication" we have made a course Slack channel: NbiAppliedML2022.slack.com. |
Exam: | Final project (possibly virtual) presentations on Wednesday the 15th and Thursday the 16th of June all day (9:00-17:00+). |
Evaluation: | Initial project (40%), and final project (60%), evaluated by lecturers following the Danish 7-step scale. |
"Best day of my life!" (Pressumably at the University, red.) [Christian M. Clausen, on the day of final project presentations, 2019] "Student 1: Damn..." "Student 2: I was just thinking what a shame you didn't get to see a whole classroom worth of 'damn' faces! But the feeling is there." [Reaction in Zoom chat, after having explained the capabilities of Reinforcement Learning examplified by AlphaZero, 2020] [And I got to see the reaction the year before!] "Troels is the perfect shepherd guiding relatively inexperienced statisticians to machine learning in an approachable and fun way." [Anon, course evaluation, 2021] "This course (and Applied Statistics) were among the most useful and insightful courses I have taken in my academic life." [Petroula Karakosta, 2022] |