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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
 
Maxim Khomiakov
Maxim Khomiakov
 
Sayantan Sengupta
Sayantan Sengupta
 
Ahmet Baglan
Ahmet Baglan
 
Martin Jørgensen
Martin Jørgensen
 
Jakob Thorsbro
Jakob Thorsbro
 
Malte Jensen
Malte Jensen
 
Lorenzo Belgrano
Lorenzo Belgrano
 
Jonas Sonn Jørgensen
Jonas Sonn Jørgensen
 
Alessandro Catania
Alessandro Catania
 
Aksel Sylvest Obdrup
Aksel Sylvest Obdrup
 
Sebastian Daugaard
Sebastian Daugaard
 
Sara Nielsen
Sara Nielsen
 

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.

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).

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 September, 2018 THIntroduction C1
Data: Feature extraction, and visualization
211 September, 2018 THData and feature extraction C2, C3P3.1, P2.1, P3.2
318 September, 2018 THMeasures of similarity and summary statistics C4P4.1, P4.2, P4.3
425 September, 2018 THData Visualization and probability C5, C6P5.1, P5.2, P6.1
Supervised learning: Classification and regression
52 October, 2018 THDecision trees and linear regression (Project 1 due before 13:00) C7, C8P8.1, P7.1, P7.2
69 October, 2018 THOverfitting and performance evaluation C9P9.1, P9.2, P9.3
Holiday
723 October, 2018 THNearest Neighbor, Bayes and Naive Bayes C10, C11P11.1, P11.2, P10.1
830 October, 2018 THArtificial Neural Networks and Bias/Variance C12, C13P13.1, P13.2, P13.3
96 November, 2018 THAUC and ensemble methods C14, C15P14.1, P14.2, P15.1
Unsupervised learning: Clustering and density estimation
1013 November, 2018 THK-means and hierarchical clustering (Project 2 due before 13:00) C16P16.1, P16.2, P16.3
1120 November, 2018 THMixture models and density estimation C17, C18P18.1, P17.1, P17.2
1227 November, 2018 THAssociation mining C19P19.1, P16.2, P16.3
Recap
134 December, 2018 THRecap and discussion of the exam (Project 3 due before 13:00) C1-C19

(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.)

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