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Section for Cognitive Systems
DTU Compute

02452 Machine Learning (Fall 2025)

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

 
Albert Kjøller Jacobsen
Albert Kjøller Jacobsen
 
Johanna Marie Gegenfurtner
Johanna Marie Gegenfurtner
 
Tova Alenfalk
Tova Alenfalk
 
Phillip Chavarria Højbjerg
Phillip Chavarria Højbjerg
 
Alejandro Valverde Mahou
Alejandro Valverde Mahou
 
Nastaran Moradzadeh Farid
Nastaran Moradzadeh Farid
 
Mikkel Nielsen Broch-Lips
Mikkel Nielsen Broch-Lips
 
Emma Klostergaard Christensen
Emma Klostergaard Christensen
 
Zofia Agata Lenarczyk
Zofia Agata Lenarczyk
 
Julie Buch Nielsen
Julie Buch Nielsen
 
João Prazeres
João Prazeres
 
Ali Asadighafari
Ali Asadighafari
 
Samuele Dilengite
Samuele Dilengite
 

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 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 2 September, 2025 gear Introduction, data, and visualization C1, C2, C7 P2.1, P7.1
2 9 September, 2025 gear Summary statistics, similarity, Nearest Neighbor and Decision Trees C4, C12, C9 P4.2, P4.3, P4.5, P12.1, P9.1, P9.2
3 16 September, 2025 gear Computational linear algebra and PCA C3 P3.1, P3.2
4 23 September, 2025 gear Probability theory, Bayes and Naïve Bayes C5, C6, C13 P5.1, P6.1, P6.2, P13.1, P13.2
5 30 September, 2025 gear Bayesian Inference and Linear Models C8 P8.1, P8.2 Project 1 due 2 Oct.
6 7 October, 2025 gear Overfitting and cross-validation C10 C10.1, C10.2
Holiday
7 21 October, 2025 gear Performance evaluation and AUC C11, C16 P16.1, P16.2 Project 1 feedback available 23 Oct.
8 28 October, 2025 gear Artificial Neural Networks and Optimization C15 P15.1, P15.2, P15.3 (Project 1 resubmission due 30 Oct.)
9 4 November, 2025 gear Bias/Variance and Ensemble methods C14, C17 P17.1
10 11 November, 2025 bjje K-means and hierarchical clustering C18 P18.1, P18.2, P18.3 Project 2 due 13 Nov.
11 18 November, 2025 bjje Mixture models and density estimation C19, C20 P20.1, P19.1, P19.2
12 25 November, 2025 bjje Representation learning - Project 2 feedback available 27 Nov.
13 2 December, 2025 gear Recap and discussion of the exam C1-C20 (Project 2 resubmission due 4 Dec.)

(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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