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.
|1||3 September, 2019||TH||Introduction||C1|
|Data: Feature extraction, and visualization|
|2||10 September, 2019||TH||Data, feature extraction and PCA||C2, C3||P3.1, P2.1, P3.2|
|3||17 September, 2019||TH||Measures of similarity, summary statistics and probabilities||C4, C5||P4.1, P4.2, P4.3|
|4||24 September, 2019||TH||Probability densities and data Visualization||C6, C7||P6.1, P6.2, P7.1|
|Supervised learning: Classification and regression|
|5||1 October, 2019||TH||Decision trees and linear regression (Project 1 due before 13:00)||C8, C9||P9.1, P8.1, P8.2|
|6||8 October, 2019||TH||Overfitting, cross-validation and Nearest Neighbor||C10, C12||P10.1, P10.2, P12.1|
|7||22 October, 2019||TH||Performance evaluation, Bayes, and Naive Bayes||C11, C13||P13.1, 13.2, P12.2|
|8||29 October, 2019||TH||Artificial Neural Networks and Bias/Variance||C14, C15||P15.1, P15.2, P15.3|
|9||5 November, 2019||TH||AUC and ensemble methods||C16, C17||P16.1, P16.2, P17.1|
|Unsupervised learning: Clustering and density estimation|
|10||12 November, 2019||TH||K-means and hierarchical clustering (Project 2 due before 13:00)||C18||P18.1, P18.2, P18.3|
|11||19 November, 2019||TH||Mixture models and density estimation||C19, C20||P20.1, P19.1, P19.2|
|12||26 November, 2019||TH||Association mining||C21||P21.1, P18.2, P18.3|
|13||3 December, 2019||TH||Recap and discussion of the exam (Project 3 due before 13:00)||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.)
Yes. Old reports are automatically transferred by default. See description for project 1. Please do not upload old reports anew.
If you are missing a report, you will get an email around exam time to your firstname.lastname@example.org email. 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 2 weeks after handin (3 weeks for project one)
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 Piazza 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.
Yes, exam is "all aids allowed". See study handbook for details. We strongly recommend you bring a computer to the exam.
Questions about course content/reports are best asked on Piazza so all students have access to the same information.
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!
There is only a re-exam in August. More information will be announced on campusnet