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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
 
Georgios Arvanitidis
Georgios Arvanitidis
 
Magnus Ingvi Magnusson
Magnus Ingvi Magnusson
 
Jonas Busk
Jonas Busk
 
Asger Anker Sørensen
Asger Anker Sørensen
 
Peter Bjørn Jørgensen
Peter Bjørn Jørgensen
 
Thomas Nymand Nielsen
Thomas Nymand Nielsen
 
Yashar Khadem Sabaz
Yashar Khadem Sabaz
 
Nicki Skafte
Nicki Skafte
 
Georgios Papoutsakis
Georgios Papoutsakis
 

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 B303A auditorium A042 Tuesdays from 13:00-15:00 followed by exercises in B308-IT117, B308-IT127, B308-IT109, B308-IT101 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

Preliminary lecture schedule

No. Date Subject Preperation
130 August, 2016 THIntroduction C1
Data: Feature extraction, and visualization
26 September, 2016 THData and feature extraction C2, C3. (P3.1, P2.1, P3.2)
313 September, 2016 THMeasures of similarity and summary statistics C4. (P4.1, P4.2, P4.3)
420 September, 2016 THData Visualization and probability C5, C6. (P5.1, P5.2, P6.1)
Supervised learning: Classification and regression
527 September, 2016 MMDecision trees and linear regression (Hand in project 1 before 13:00) C7, C8. (P8.1, P7.1, P7.2)
64 October, 2016 MMOverfitting and performance evaluation C9. (P9.1, P9.2, P9.3)
711 October, 2016 MMNearest Neighbor, Bayes and Naive Bayes C10, C11. (P11.1, P11.2, P10.1)
Holiday
825 October, 2016 MMArtificial Neural Networks and Bias/Variance C12, C13. (P13.1, P13.2, P13.3)
91 November, 2016 MMAUC and ensemble methods C14, C15. (P14.1, P14.2, P15.1)
Unsupervised learning: Clustering and density estimation
108 November, 2016 MMK-means and hierarchical clustering (Hand in project 2 before 13:00) C16. (P16.1, P16.2, P16.3)
1115 November, 2016 MMMixture models and density estimation C17, C18. (P18.1, P17.1, P17.2)
1222 November, 2016 MMAssociation mining C19. (P19.1, P19.2, P19.3)
Recap
1329 November, 2016 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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