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Section for Cognitive Systems
DTU Compute

02450 Introduction to Machine Learning and Data Mining - Spring 2024

Bjørn Sand Jensen
Bjørn Sand Jensen
 
Georgios Arvanitidis
Georgios Arvanitidis
 
Tue Herlau
Tue Herlau
 
Morten Mørup
Morten Mørup
 
Mikkel N. Schmidt
Mikkel N. Schmidt
 
_________________
Teaching Assistants:

 
Alejandra Navarro Castillo
Alejandra Navarro Castillo
 
Andreas Hansen Bagge
Andreas Hansen Bagge
 
Felix Oscar Ærtebjerg (absert in W8)
Felix Oscar Ærtebjerg (absert in W8)
 
Maja Hjuler (absent in W2, room A083 in W11)
Maja Hjuler (absent in W2)
 
Diego Rodriguez Gordo
Diego Rodriguez Gordo (absent in W1 and W7)
 
Farkas Attila Jakab (absent W5)
Farkas Attila Jakab (absent W5)
 
Nándor Takács
Nándor Takács
 
Erik Buur Christensen
Erik Buur Christensen
 
András Bence Schin
András Bence Schin
 
Anna Emilie Jennow Wedenborg
Anna Emilie Jennow Wedenborgm (absent W5-8)
 
Saeid Barzegarkhordehbalagh
Saeid Barzegarkhordehbalagh (absent W9-13)
 
William Sandholt Hansen (absent in W1)
William Sandholt Hansen (absent in W1)
 
Chiku Parida
Chiku Parida
 
Jeremi Wojciech Ledwon
Jeremi Wojciech Ledwon
 
No dedicated TA (seek guidance in adjoining rooms)
No dedicated TA (seek guidance in adjoining rooms)
 

Machine learning and data mining

The course is designed around a data modeling framework shown in the figure. Each lecture/assignment will focus a subset 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.

Due to restrictions on the auditorium capacity, we will stream the lecture to Building 116 auditorium 83. Seats in Building 116 auditorium 81 and Building 116 auditorium 83 will be allocated on a first come, first served principle. You can use the rooms allocated to exercises (except H013 and H015) to stream the lecture yourself (feel free to use the projectors).

We will record the lecture and make available online..

Exercises

Exercises will take place after lectures Tuesdays from 15:00-17:00.

There is room capacity for all signed up students at the exercises every week.

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 discussion forum.

Teachers

Lecture schedule

Week Date Who Subject Reading Homework Project deadlines
(17:00 CET via DTU Learn)
1 30 January, 2024 BJJE Introduction C1
Data: Feature extraction, and visualization
2 6 February, 2024 BJJE Data, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
3 13 February, 2024 BJJE Measures of Similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
4 20 February, 2024 BJJE Probability densities and data visualization C6, C7 P6.1, P6.2, P7.1
Supervised learning: Classification and regression
5 27 February, 2024 BJJE Decision trees and linear regression C8, C9 P9.1, P8.1, P8.2 Project 1 due 29 February
6 5 March, 2024 BJJE Overfitting, cross-validation and Nearest Neighbor C10, C12 P10.1, P10.2, P12.1
7 12 March, 2024 BJJE Performance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2 Project 1 feedback available 14 March
8 19 March, 2024 BJJE Artificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3 (Project 1 resubmission due 21 March)
Holiday
9 2 April, 2024 BJJE AUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
Unsupervised learning: Clustering and density estimation
10 9 April, 2024 GEAR K-means and hierarchical clustering C18 P18.1, P18.2, P18.3 Project 2 due 11 April
11 16 April, 2024 GEAR Mixture models and density estimation C19, C20 P20.1, P19.1, P19.2
12 23 April, 2024 GEAR Association mining C21 P21.1, P18.2, P18.3 Project 2 feedback available 25 April
Summary
13 30 April, 2024 BJJE Recap and discussion of the exam C1-C21 (Project 2 resubmission due 2 May)

(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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