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Section for Cognitive Systems
DTU Compute

02450 Introduction to Machine Learning and Data Mining

Jes Frellsen
Jes Frellsen
 
Morten Mørup
Morten Mørup
 
Tue Herlau
Tue Herlau
 
Mikkel N. Schmidt
Mikkel N. Schmidt
 
Kristoffer Jon Albers
Kristoffer Jon Albers
 
Oliver Kinch Hermansen
Oliver Kinch Hermansen
 
Qahir Siavash Yousefi
Qahir Siavash Yousefi
 
Mikkel Mathiasen
Mikkel Mathiasen
 
Rasmus Hannibal Tirsgaard
Rasmus Hannibal Tirsgaard
 
Christian Hinge
Christian Hinge
 
Raül Pérez i Gonzalo
Raül Pérez i Gonzalo
 
Oldouz Majidi
Oldouz Majidi
 
Eskild Børsting Sørensen
Eskild Børsting Sørensen
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Michael Rahbek
Michael Rahbek
 
Rasmus Møller Larsen
Rasmus Møller Larsen
 
Fabian Martin Mager
Fabian Martin Mager
 
Artur Tomasz Niewiadomski
Artur Tomasz Niewiadomski
 
Mikkel Godsk Jørgensen
Mikkel Godsk Jørgensen
 
Asbjørn Vitus Bering Nielsen
Asbjørn Vitus Bering Nielsen
 
Ian Beissmann
Ian Beissmann
 
Peter Mørch Groth
Peter Mørch Groth
 
Nikolaos Nakis
Nikolaos Nakis
 
Alison Marie Sandrine Pouplin
Alison Marie Sandrine Pouplin
 
Alessandro Pasta
Alessandro Pasta
 

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 306 auditorium 32, 33, and 34 Tuesdays from 13:00-15:00.

Due to restrictions on the auditorium capacity, you will not be able to attend lecture in person every week. For people not attending the lectures in person, it is possible to stream the lectures online. On the DTU Inside course page, you can see which lectures you are allowed to attend in person.

We will try to record the lecture and make available online.

Exercises

Exercises will take place after lectures Tuesdays from 15:00-17:00.

There is room capacity for all signed up students at the exercises every week.

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 (room capacity square brackets and programming language in parentheses):

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 Inside 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
11 September, 2020 JFIntroduction C1
Data: Feature extraction, and visualization
28 September, 2020 JFData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
315 September, 2020 MMMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
422 September, 2020 JFProbability densities and data Visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
529 September, 2020 JFDecision trees and linear regression C8, C9 P9.1, P8.1, P8.2
66 October, 2020 JFOverfitting, cross-validation and Nearest Neighbor (Project 1 due before 13:00) C10, C12 P10.1, P10.2, P12.1
Holiday
720 October, 2020 JFPerformance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2
827 October, 2020 JFArtificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3
93 November, 2020 JFAUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
Unsupervised learning: Clustering and density estimation
1010 November, 2020 JFK-means and hierarchical clustering C18 P18.1, P18.2, P18.3
1117 November, 2020 JFMixture models and density estimation (Project 2 due before 13:00) C19, C20 P20.1, P19.1, P19.2
1224 November, 2020 JFAssociation mining C21 P21.1, P18.2, P18.3
Recap
131 December, 2020 JFRecap and discussion of the exam 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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