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 (26/04-25/06 2021). |
Where: |
To begin with, online only. We will then see, how things develop. |
Format: |
Shorter lectures followed by computer exercises and discussion with emphasis on experience and projects. |
Text book: |
References to Elements of Statistical Learning II.
|
Additional literature: |
We (i.e. you) will make extensive use of online ML resources, collected for this course.
|
Programming: |
Primarily Python 3.6+ with a few packages on top, though this is an individual choice. |
Code repository: |
AppliedML2021 GitHub respository. |
Communication: |
Messages through Absalon, lectures and exercises given live via Zoom. |
Collaborative tools: |
We have made a course Slack channel: NbiAppliedML2021.slack.com, but you're of course welcome to use "anything" at will. |
Exam: |
Final project (possibly virtual) presentations on Wednesday the 16th and Thursday the 17th of June all day (9:00-17:00+). |
Evaluation: |
Small project (40%), and final project (60%), evaluated by lecturers following the Danish 7-step scale. |