space
Section for Cognitive Systems
DTU Compute

02450 Introduction to Machine Learning and Data Mining

Morten Mørup
Morten Mørup
 
Tue Herlau
Tue Herlau
 
Mikkel N. Schmidt
Mikkel N. Schmidt
 
Florian Gawrilowicz
Florian Gawrilowicz
 
Sayantan Sengupta
Sayantan Sengupta
 
Rasmus M. T. Høegh
Rasmus M. T. Høegh
 
Malte K. E. Jensen
Malte K. E. Jensen
 
Sebastian Mira
Sebastian Mira
 
Mads O. Jakobsen
Mads O. Jakobsen
 
Andreas Munk
Andreas Munk
 
Benjamin J&uumlttner
Benjamin Jüttner
 
Frederik B. H&uumlttel
Frederik B. Hüttel
 
Quoc Tien Au
Quoc Tien Au
 
Lorenzo Belgrano
Lorenzo Belgrano
 
Paolo A. Mesiano
Paolo A. Mesiano
 

Machine learning and data mining

The course is designed around a data modeling framework shown in the figure. Each lecture/assignment will focus on an aspect of the data modeling framework.

data modeling framework

We emphasize the holistic view of modeling in order to motivate and stress the relevance of individual components and building blocks, disseminate the obtained competence (see the course learning obejctives), and make them applicable for a broad spectrum of engineering problems in e.g. biomedical engineering, chemistry, electrical engineering, and informatics.

Resources

Location

The lectures will take place in building 303A auditorium 41 and 42 Tuesdays from 13:00-15:00 followed by exercises in

Python: Building 308 rooms 101, 109, 117, 1st floor area north and south

Matlab: Building 210 rooms 162, 168 as well as building 303A auditorium 42

R: Building 308 room 127

from 15:00-17:00. Please bring a laptop computer for the exercises.

Reading material, lecture slides and exercises

The course will use lecture notes and other freely available material. Lecture notes, slides, course assignment instructions etc. is available at the DTU Campusnet course page (requires formal enrolment to the course).

Course description

A description of the course can be found at the DTU Coursebase

Online help and support

Online help and support is available through the Piazza course platform.

Teacher

Lecture schedule

No. Date Subject Preparation
15 September, 2017 MMIntroduction C1
Data: Feature extraction, and visualization
212 September, 2017 MMData and feature extraction C2, C3. (P3.1, P2.1, P3.2)
319 September, 2017 MMMeasures of similarity and summary statistics C4. (P4.1, P4.2, P4.3)
426 September, 2017 MMData Visualization and probability C5, C6. (P5.1, P5.2, P6.1)
Supervised learning: Classification and regression
53 October, 2017 MMDecision trees and linear regression (Hand in project 1 before 13:00) C7, C8. (P8.1, P7.1, P7.2)
610 October, 2017 MMOverfitting and performance evaluation C9. (P9.1, P9.2, P9.3)
Holiday
724 October, 2017 MMNearest Neighbor, Bayes and Naive Bayes C10, C11. (P11.1, P11.2, P10.1)
831 October, 2017 MMArtificial Neural Networks and Bias/Variance C12, C13. (P13.1, P13.2, P13.3)
97 November, 2017 MMAUC and ensemble methods C14, C15. (P14.1, P14.2, P15.1)
Unsupervised learning: Clustering and density estimation
1014 November, 2017 MMK-means and hierarchical clustering (Hand in project 2 before 13:00) C16. (P16.1, P16.2, P16.3)
1121 November, 2017 MMMixture models and density estimation C17, C18. (P18.1, P17.1, P17.2)
1228 November, 2017 MMAssociation mining C19. (P19.1, P16.2, P16.3)
Recap
135 December, 2017 MMRecap and discussion of the exam (Hand in project 3 before 13:00) C1-C19

(Cx refers to Chapter x of the course notes. Px.y refers to problem number y in chapter x of the course notes.
The first listed problem will be that weeks discussion question at the exercises.)

DTU logo space
space