space
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
 
Vittorio Carmignani
Vittorio Carmignani
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Oliver Kinch Hermansen
Oliver Kinch Hermansen
 
Christian Hinge
Christian Hinge
 
Morten Wehlast Jørgensen
Morten Wehlast Jørgensen
 
Mikkel Mathiasen
Mikkel Mathiasen
 
Raül Pérez i Gonzalo
Raül Pérez i Gonzalo
 
Andreas Lindhardt Plesner
Andreas Lindhardt Plesner
 
Rasmus Hannibal Tirsgaard
Rasmus Hannibal Tirsgaard
 
Frederik Rahbæk Warburg
Frederik Rahbæk Warburg
 
Qahir Abdul Yousefi
Qahir Abdul Yousefi
 
Oldouz Majidi
Oldouz Majidi
 
Naveen Karun Somasundaram
Naveen Karun Somasundaram
 

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 and 43 (except week 7, Tuesday 17th of March, where we will be in building 306 auditorium 33 and 34) 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
14 February, 2020 MMIntroduction C1
Data: Feature extraction, and visualization
211 February, 2020 MMData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
318 February, 2020 MMMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
425 February, 2020 MMProbability densities and data Visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
53 March, 2020 MMDecision trees and linear regression (Project 1 due before 13:00) C8, C9 P9.1, P8.1, P8.2
610 March, 2020 MMOverfitting, cross-validation and Nearest Neighbor C10, C12 P10.1, P10.2, P12.1
717 March, 2020 MMPerformance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2
824 March, 2020 MMArtificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3
931 March, 2020 MMAUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
Holiday
Unsupervised learning: Clustering and density estimation
1014 April, 2020 MMK-means and hierarchical clustering (Project 2 due before 13:00) C18 P18.1, P18.2, P18.3
1121 April, 2020 MMMixture models and density estimation C19, C20 P20.1, P19.1, P19.2
1228 April, 2020 MMAssociation mining C21 P21.1, P18.2, P18.3
Recap
135 May, 2020 MMRecap 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

DTU logo space
space