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Section for Cognitive Systems
DTU Compute

02451 Introduction to Machine Learning (Spring 2026)

Bjørn Sand Jensen
Bjørn Sand Jensen
 
Georgios Arvanitidis
Georgios Arvanitidis
 
Morten Mørup
Morten Mørup
 
_________________
Teaching Assistants:

 
Phillip Chavarria Højbjerg
Phillip Chavarria Højbjerg
 
Sofie Bak
Sofie Bak
 
Magdalena Zydorczak
Magdalena Zydorczak
 
Jesper Rask Pedersen (absent W6)
Jesper Rask Pedersen (absent W6)
 
Alessandra Carrara (absent W9)
Alessandra Carrara (absent W9)
 
Amna Hadzihalilovic
Amna Hadzihalilovic
 
Kyle Nathan Yahya
Kyle Nathan Yahya
 
João Prazeres (absent W1)
João Prazeres (absent W1)
 
Oscar Lomborg
Oscar Lomborg
 
Lampros Spanos
Lampros Spanos
 
Julie Buch Nielsen  (absent W3)
Julie Buch Nielsen (absent W3)
 
Ali Asadighafari
Ali Asadighafari
 
Frederik Raae Johansen
Frederik Raae Johansen
 
No TA assigned but reserved for 02451
No TA assigned but reserved for 02451
 

The course is designed around a data modeling framework shown in the figure. Each lecture/assignment will focus on 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 objectives), 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 82. Seats in Building 116 auditorium 81 and Building 116 auditorium 82 will be allocated on a first come, first served principle. You can use the rooms allocated to exercises to stream the lecture yourself (Zoom link on DTU Learn).

We will record the lecture and make available online on DTU Learn.

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 Python. The exercise rooms are (room capacity square brackets and programming language in parentheses):

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 3 February, 2026 bjje Introduction, data, and visualization C1, C2, C7 P2.1, P7.1 A1 due 26 February
2 10 February, 2026 bjje Summary statistics, similarity, and nearest neighbor C4, C12 P4.2, P4.3, P4.5, P12.1 A2 due 26 February
3 17 February, 2026 bjje Computational linear algebra and PCA C3 P3.1, P3.2 A3 due 26 February
4 24 February, 2026 bjje Probability theory, Bayes and Naïve Bayes C5, C6, C13 P5.1, P6.1, P6.2, P13.1, P13.2 A4 due 19 March
5 3 March, 2026 bjje Bayesian Inference and Linear Models C6.4, C8 P8.1, P8.2 A5 due 19 March
6 10 March, 2026 bjje Decision trees, overfitting and cross-validation C9, C10 P9.1, P9.2, P10.1, P10.2 A6 due 19 March
7 17 March, 2026 bjje Performance evaluation and AUC C11, C16 P16.1, P16.2 A7 due 16 April
8 24 March, 2026 bjje Artificial Neural Networks and Optimization C15 P15.1, P15.2, P15.3 A8 due 16 April
Holiday
9 7 April, 2026 bjje Bias/Variance and Ensemble methods C14, C17 P17.1 A9 due 16 April
10 14 April, 2026 gear K-means and hierarchical clustering C18 P18.1, P18.2, P18.3 A10 due 10 May
11 21 April, 2026 gear Mixture models and density estimation C19, C20 P20.1, P19.1, P19.2 A11 due 10 May
12 28 April, 2026 gear Representation learning See DTU Learn A12 due 10 May
13 5 May, 2026 bjje Recap and discussion of the exam 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. Ax refers to assignment in week x.
The first listed problem will be that weeks discussion question at the exercises.)

FAQ

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