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.
If you are enrolled in the course you can access material and participate in the course through the DTU Learn homepage.
The lectures will take place in Building 116 auditorium 081 and 083 on Tuesdays from 13:00-15:00.
If you cannot attend the lectures in person, it is possible to stream the lectures online, and all lectures will be recorded and made available online.
Exercises will take place after lectures on Tuesdays from 15:00-17:00.
You will be able to attend the exercises online, however physical attendance is preferred and highly recommended.
We expect you will have access to your own laptop/computer during the exercise sessions. 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.
Please bring a laptop computer for the exercises. The exercises will be available in
Virtual exercise rooms on Microsoft Teams :
|1||30 August, 2022||GA||Introduction||C1|
|Data: Feature extraction, and visualization|
|2||6 September, 2022||GA||Data, feature extraction and PCA||C2, C3||P3.1, P2.1, P3.2|
|3||13 September, 2022||GA||Measures of similarity, summary statistics and probabilities||C4, C5||P4.1, P4.2, P4.3|
|4||20 September, 2022||GA||Probability densities and data visualization||C6, C7||P6.1, P6.2, P7.1|
|Supervised learning: Classification and regression|
|5||27 September, 2022||GA||Decision trees and linear regression||C8, C9||P9.1, P8.1, P8.2|
|6||4 October, 2022||GA||Overfitting, cross-validation and Nearest Neighbor (Project 1 due before 13:00)||C10, C12||P10.1, P10.2, P12.1|
|7||11 October, 2022||GA||Performance evaluation, Bayes, and Naive Bayes||C11, C13||P13.1, 13.2, P12.2|
|8||25 October, 2022||GA||Artificial Neural Networks and Bias/Variance||C14, C15||P15.1, P15.2, P15.3|
|9||1 November, 2022||GA||AUC and ensemble methods||C16, C17||P16.1, P16.2, P17.1|
|Unsupervised learning: Clustering and density estimation|
|10||8 November, 2022||BJ||K-means and hierarchical clustering||C18||P18.1, P18.2, P18.3|
|11||15 November, 2022||BJ||Mixture models and density estimation (Project 2 due before 13:00)||C19, C20||P20.1, P19.1, P19.2|
|12||22 November, 2022||BJ||Association mining||C21||P21.1, P18.2, P18.3|
|13||29 November, 2022||GA||Recap and discussion of the exam||C1-C21|
(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.)
Questions about course content/reports are best asked on the discussion forum so all students have access to the same information.
Yes, but please fill out everything you can beforehand, including your participation. I will edit it and I am often unsure what it needs to say.
Please send an e-mail or phone the teachers listed above.
I can (and will!) add you to the group so you can see the material, but to join the course (and be at the exam) you have to enroll. You can do this through your DTU study planner. If the study planner does not work, you have to contact the study administration by email. My default position is to approve all new students. Always check your exam registration when they are published.
Yes. Old reports are automatically transferred by default. See description for project 1. Please do not upload old reports anew unless you have changed the content
If you are missing a report, I will send you an email to your firstname.lastname@example.org shortly after the exam. No email, no problem.
Reports are not graded but evaluated. By the DTU rules we cannot give you a numerical score. However, the feedback from TA's should provide a good indication.
Under normal circumstances, feedback should be available about 3 weeks after you handed in project 1, and 2 weeks after you handed in project 2.
If feedback is delayed more than a day or two please send me an email.
Reports are not passed/not passed. Even a very poor report is better than not handing in.
No. Some students complete their projects on their own; if you don't have a group, or things are going south with your group, you have to fix it within the deadline.
Please use the discussion forum to find team members.
Only after explicit permission from me due to resource constraints.
We are reasonable if you are handing in late due to extraordinary circumstances, but otherwise we take the deadline serious. It would not be fair to all those students who meet the deadline if we accepted late handins without consequences.
You must bring a computer to the exam since the exam use electronic hand-ins. You can also bring ipads, etc. The only limitation is you cannot communicate with others during the exam.
The examination time-table can be found at https://www.dtu.dk/english/education/examination-timetable and to find 02450 you need the schema block which ca be found here: https://kurser.dtu.dk/course/02450
Please look at the official FAQ about exams: https://www.inside.dtu.dk/da/undervisning/regler/regler-for-eksamen/faq-om-skriftlig-eksaminer
In the spring semester there is a re-exam in August. For the fall semester the next exam is in the following spring semester.
If you encounter IT problems and it appears clear something is wrong with a page or your account please submit a ticket to the (usually very responsive) IT support: https://itservice.ait.dtu.dk
Yes, after the course has completed, I will make a call for teaching assistants. Pay is more than 200DKR per hour and there is preperation time!