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
 
Stefano Cerri
Stefano Cerri
 
Sveinn Pálsson
Sveinn Pálsson
 
Alessandro Catania
Alessandro Catania
 
Malte Jensen
Malte Jensen
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Aksel Sylvest Obdrup
Aksel Sylvest Obdrup
 
Sayantan Sengupta
Sayantan Sengupta
 
Gabriel Pons
Gabriel Pons
 
Oldouz Majidi
Oldouz Majidi
 
Niels Overby
Niels Overby
 
Cilie Werner Feldager Hansen
Cilie Werner Feldager Hansen
 
Mathieu Patrick Emile Chatelier
Mathieu Patrick Emile Chatelier
 

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 42 and 41 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
15 February, 2019 THIntroduction C1
Data: Feature extraction, and visualization
212 February, 2019 MMData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
319 February, 2019 THMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
426 February, 2019 THProbability densities and data Visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
55 March, 2019 THDecision trees and linear regression (Project 1 due before 13:00) C8, C9 P9.1, P8.1, P8.2
612 March, 2019 THOverfitting and performance evaluation C10 P10.1, P10.2, P10.3
719 March, 2019 THNearest Neighbor, Bayes and Naive Bayes C11, C12 P12.1, P12.2, P11.1
826 March, 2019 THArtificial Neural Networks and Bias/Variance C13, C14 P14.1, P14.2, P14.3
92 April, 2019 THAUC and ensemble methods C15, C16 P15.1, P15.2, P16.1
Unsupervised learning: Clustering and density estimation
109 April, 2019 THK-means and hierarchical clustering (Project 2 due before 13:00) C17 P17.1, P17.2, P17.3
Holiday
1123 April, 2019 THMixture models and density estimation C18, C19 P19.1, P18.1, P18.2
1230 April, 2019 THAssociation mining C20 P20.1, P17.2, P17.3
Recap
137 May, 2019 THRecap and discussion of the exam (Project 3 due before 13:00) C1-C20

(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