Content: |
Graduate course on Machine Learning and Big Data usage in science. |
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 (20/04-17/06 2020). |
Where: |
Mondays: Lectures (13-14) + exercises (14-17) in BioCenter 4-0-32. |
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Wednesdays: Lectures (9-10) + exercises (10-12) in NBB 01.0.G.064/070 and Lectures (13-14) + exercises (14-17) in BioCenter 4-0-32. |
Format: |
Shorter lectures followed by computer exercises and discussion with emphasis on experience and projects. |
Text book: |
References to Elements of Statistical Learning II.
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Additional literature: |
We (and you) will make extensive use of online ML resources, collected throughout the course.
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Language: |
English (occational Danish utterings!). All exercises, problem sets, notes, etc. are in English. |
Programming: |
Primarily Python 3.6+ with a few packages on top, though this is an individual choice. |
Communication: |
Lectures and exercises will be given live via Zoom and a course Slack channel: nbiappliedml2020.slack.com. |
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Discord is also a widely used channel, and this course has a channel under General HCO studying. |
Exam: |
Final project (possibly virtual) presentations on Wednesday the 10th 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. |
Credits: |
7.5 ECTS
(1/8 academic years work, that is 187.5-225 hours of work, thus about 23-28 hours weekly). |