02450 Introduction to Machine Learning and Data Modeling

Tan et al. Introduc-
tion to Data Mining

Morten Mørup

Mikkel N. Schmidt

Jens Madsen

Kit Melissa Larsen

Machine learning and data modeling

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.

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

Location

Lectures and computer assignment takes place in Building 303, Auditorium 41 (lectures), and Databar 047 and 048 (exercises), Spring 2013.

Reading material

The course is based on the book: Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, "Introduction to Data Mining."
The book is available from Polyteknisk Boghandel

Lecture slides and exercises

Lecture slide handouts, assignment instruction 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

Teacher

Morten Mørup (MM), mm@imm.dtu.dk
Mikkel N. Schmidt (MNS), mns@imm.dtu.dk
Jens Madsen (JENMA), jenma@imm.dtu.dk
Kit Melissa Larsen (KMELA), kmela@imm.dtu.dk

Lecture schedule

No.	Date	Teacher	Subject	Reading material
1	5 Feb 2013	MM	Introduction	1.1-1.4
Data: Feature extraction, and visualization
2	12 Feb 2013	MM	Data and feature extraction	2.1-2.3 + (A) + B.1
3	19 Feb 2012	MM	Measures of similarity and summary statistics	2.4 + 3.1-3.2 + C1-C2
4	26 Feb 2013	MM	Data visualization	3.3
Supervised learning: Classification and regression
5	5 Mar 2013	MM	Decision trees and linear regression	4.1-4.3 + D
6	12 Mar 2013	MM	Overfitting and performance evaluation	4.4-4.6
7	19 Mar 2013	MM	Nearest neighbor, naive Bayes, and artificial neural networks	5.2-5.4
	26 Mar 2013		Easter Holiday
8	2 Apr 2013	MM	Ensemble methods and multi-class classifiers	5.6-5.8
Unsupervised learning: Clustering and density estimation
9	9 Apr 2013	MM	K-means and hierarchical clustering	8.1-8.3+8.5.7
10	16 Apr 2013	MM	Mixture models and association mining	9.2.2 + 6.1-6.3
11	23 Apr 2013	MM	Density estimation and anomaly detection	10.1-10.4
Machine learning and data modelling in practice
12	30 Apr 2013	MM	Putting it all together: Summary and overview
13	7 May 2013	MM	Project presentation