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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
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Oldouz Majidi
Oldouz Majidi
 
Cilie Werner Feldager Hansen
Cilie Werner Feldager Hansen
 
Guðrún Atladóttir
Guðrún Atladóttir
 
Rasmus Hannibal Tirsgaard
Rasmus Hannibal Tirsgaard
 
Yihao Sun
Yihao Sun
 
Thomas Nilsson
Thomas Nilsson
 
Eskild Børsting Sørensen
Eskild Børsting Sørensen
 
Jonas Juhler-Nøttrup
Jonas Juhler-Nøttrup
 
Qahir Abdul Yousefi
Qahir Abdul Yousefi
 
Zihao Wang
Zihao Wang
 
David Ribberholt Ipsen
David Ribberholt Ipsen
 
Ali Mohebbi
Ali Mohebbi
 
Anton Baht
Anton Baht
 
Gísli Tómas Gudjónsson
Gísli Tómas Gudjónsson
 
Moein Jahanbani Veshareh
Moein Jahanbani Veshareh
 

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

Lectures

The lectures will take place in building 303A auditorium 41, 42, 44, and 48 Tuesdays from 13:00-15:00. If you cannot attend the lectures in person, it is possible to stream the lectures online, and all lectures will be recorded and made available as podcasts.

Exercises

Exercises will take place after lectures Tuesdays from 15:00-17:00. Please bring a laptop computer for the exercises. The exercises will be available in Matlab, R, and Python and we recommend selecting a language you are familiar with. If you are unfamiliar with any of the languages, we recommend Matlab. The exercise rooms are:

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).

Online demos

We have developed several online demos which illustrates key concepts from the course. The topics discussed currently includes PCA, regression, classification and density estimation.

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.

Teachers

Lecture schedule

No. Date Subject Reading Homework
13 September, 2019 THIntroduction C1
Data: Feature extraction, and visualization
210 September, 2019 THData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
317 September, 2019 THMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
424 September, 2019 THProbability densities and data Visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
51 October, 2019 THDecision trees and linear regression (Project 1 due before 13:00) C8, C9 P9.1, P8.1, P8.2
68 October, 2019 THOverfitting, cross-validation and Nearest Neighbor C10, C12 P10.1, P10.2, P12.1
Holiday
722 October, 2019 THPerformance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2
829 October, 2019 THArtificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3
95 November, 2019 THAUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
Unsupervised learning: Clustering and density estimation
1012 November, 2019 THK-means and hierarchical clustering (Project 2 due before 13:00) C18 P18.1, P18.2, P18.3
1119 November, 2019 THMixture models and density estimation C19, C20 P20.1, P19.1, P19.2
1226 November, 2019 THAssociation mining C21 P21.1, P18.2, P18.3
Recap
133 December, 2019 THRecap and discussion of the exam (Project 3 due before 13:00) C1-C21

(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.)

FAQ

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