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: | Programming experience (essential, preferably in Python) and Math (calculus and linear algebra). |
When: | Mondays 13-14 / 14-17 and Wednesdays 9-10 / 10-12 & 13-14 / 14-17 for lectures/exercises (Week Schedule Group C). |
Where (lectures): | Mondays: Library Room at DIKU (bib 4-0-17, right next to Lille UP1). Wednesdays: Lille UP1 at DIKU, |
Where (exercises): | Mondays: Biocenter 4-0--2, 4-0-10, 4-0-32, Wednesday morning: DIKU 1-0-18, 1-0-26, and 1-0-30, Wednesday afternoon: Biocenter 4-0-02 and 2-2-07/09, see KU Room Schedule plan. |
Format: | Shorter lectures followed by computer exercises and discussion with emphasis on application and projects. |
Text book: | References to (the excellent!) Applied Machine Learning by David Forsyth. |
Suppl. literature: | We (i.e. you) will make extensive use of online ML resources, collected for this course. |
Programming: | Primarily Python 3.12 with a few packages on top, though this is an individual choice. |
Code repository: | All code we provide can be found in the AppliedML2025 GitHub respository. |
Communication: | All announcements will be given through Absalon. To reach me, Email is preferable. |
Initial Project: | Initial project (a la Kaggle competition) to be submitted Sunday the 18th of May at 22:00. |
Final Project: | Final project (Exam) presentations on Wednesday the 11th (all day) and Thursday the 12th of June (morning). |
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] [Fortunately, 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] "I applaud the delivery with hands-on tutorial sessions, supported by overview lectures. The assessments excellently supported the learning with the initial project helping us get over the initial bump, and the group project showing us how to apply ML to our own interests. 5/5 stars!" [Alice Patig, Ph.D. student at DTU, 2023] "I have really enjoyed working on the final project, as it becomes super clear how important data preparation is. I also find that we discuss possibilities of using almost every ML method we've covered to tackle different issues in preparing, handling, and evaluating data." [Anon, course evaluation, 2024] |