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Section for Cognitive Systems
DTU Compute

02450 Introduction to Machine Learning and Data Mining

Jes Frellsen
Jes Frellsen
 
Tommy Sonne Alstrøm
Tommy Sonne Alstrøm
 
Tue Herlau
Tue Herlau
 
Morten Mørup
Morten Mørup
 
Mikkel N. Schmidt
Mikkel N. Schmidt
 
Peter Mørch Groth
Peter Mørch Groth
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Qahir Siavash Yousefi
Qahir Siavash Yousefi
 
Oldouz Majidi
Oldouz Majidi
 
Alvaro Carrera Cardeli
Alvaro Carrera Cardeli
 
Emil Helgren
Emil Helgren
 
Paul Jeremy Simon
Paul Jeremy Simon
 
Dea Frøding Skipper
Dea Frøding Skipper
 
Jens Parslov
Jens Parslov
 
Sai Shaurya Iyer
Sai Shaurya Iyer
 
Clément Erup Larsen
Clément Erup Larsen
 
Gonzalo Eduardo Mazzini
Gonzalo Eduardo Mazzini
 
Jonas Thusgaard Elsborg
Jonas Thusgaard Elsborg
 
Gisli Björn Helgason
Gisli Björn Helgason
 
Simon Nørby Knudsen
Simon Nørby Knudsen
 
Alvils Sture
Alvils Sture
 
Sofia Amanda de Lellis Stroustrup
Sofia Amanda de Lellis Stroustrup
 
Federico Bergamin
Federico Bergamin
 
Hugo Henri Joseph Sénétaire
Hugo Henri Joseph Sénétaire
 

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

DTU Learn

If you are enrolled in the course you can access material and participate in the course through the DTU Learn homepage.

Lectures

The lectures will take place in building 116, auditorium 81 on Tuesdays from 13:00-15:00.

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 Python. The exercise rooms are (room capacity square brackets and programming language in parentheses):

Exercises on Microsoft Teams :

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 learn 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 DTU Learn discussion forum.

Teachers

Lecture schedule

No. Date Subject Reading Homework
131 August, 2021 JFIntroduction C1
Data: Feature extraction, and visualization
27 September, 2021 JFData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
314 September, 2021 JFMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
421 September, 2021 JFProbability densities and data visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
528 September, 2021 TADecision trees and linear regression C8, C9 P9.1, P8.1, P8.2
65 October, 2021 TAOverfitting, cross-validation and Nearest Neighbor (Project 1 due before 13:00) C10, C12 P10.1, P10.2, P12.1
712 October, 2021 TAPerformance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2
Holiday
826 October, 2021 JFArtificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3
92 November, 2021 JFAUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
Unsupervised learning: Clustering and density estimation
109 November, 2021 TAK-means and hierarchical clustering C18 P18.1, P18.2, P18.3
1116 November, 2021 JFMixture models and density estimation (Project 2 due before 13:00) C19, C20 P20.1, P19.1, P19.2
1223 November, 2021 JFAssociation mining C21 P21.1, P18.2, P18.3
Recap
1330 November, 2021 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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