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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
 
Philip Johan Havemann Jørgensen
Philip Johan Havemann Jørgensen
 
Marco Dal Farra Kristensen
Marco Dal Farra Kristensen
 
Thomas Nymand Nielsen
Thomas Nymand Nielsen
 
Nicki Skafte
Nicki Skafte
 
Niels Bruun Ipsen
Niels Bruun Ipsen
 
Emilie Knudsen Brun
Emilie Knudsen Brun
 
Maciej Jan Korzepa
Maciej Jan Korzepa
 

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 116 auditorium 81 Tuesdays from 13:00-15:00 followed by exercises in building 210 rooms 002,008,018, and 148 as well as building 127 room 012 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
131 January, 2017 MMIntroduction C1
Data: Feature extraction, and visualization
27 February, 2017 MMData and feature extraction C2, C3. (P3.1, P2.1, P3.2)
314 February, 2017 MMMeasures of similarity and summary statistics C4. (P4.1, P4.2, P4.3)
421 February, 2017 MMData Visualization and probability C5, C6. (P5.1, P5.2, P6.1)
Supervised learning: Classification and regression
528 February, 2017 MMDecision trees and linear regression (Hand in project 1 before 13:00) C7, C8. (P8.1, P7.1, P7.2)
67 March, 2017 MMOverfitting and performance evaluation C9. (P9.1, P9.2, P9.3)
714 March, 2017 MMNearest Neighbor, Bayes and Naive Bayes C10, C11. (P11.1, P11.2, P10.1)
821 March, 2017 MMArtificial Neural Networks and Bias/Variance C12, C13. (P13.1, P13.2, P13.3)
928 March, 2017 MMAUC and ensemble methods C14, C15. (P14.1, P14.2, P15.1)
Unsupervised learning: Clustering and density estimation
104 April, 2017 MMK-means and hierarchical clustering (Hand in project 2 before 13:00) C16. (P16.1, P16.2, P16.3)
Holiday
1118 April, 2017 MMMixture models and density estimation C17, C18. (P18.1, P17.1, P17.2)
1225 April, 2017 MMAssociation mining C19. (P19.1, P16.2, P16.3)
Recap
132 May, 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.)

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