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||31 January, 2017||MM||Introduction||C1|
|Data: Feature extraction, and visualization|
|2||7 February, 2017||MM||Data and feature extraction||C2, C3. (P3.1, P2.1, P3.2)|
|3||14 February, 2017||MM||Measures of similarity and summary statistics||C4. (P4.1, P4.2, P4.3)|
|4||21 February, 2017||MM||Data Visualization and probability||C5, C6. (P5.1, P5.2, P6.1)|
|Supervised learning: Classification and regression|
|5||28 February, 2017||MM||Decision trees and linear regression (Hand in project 1 before 13:00)||C7, C8. (P8.1, P7.1, P7.2)|
|6||7 March, 2017||MM||Overfitting and performance evaluation||C9. (P9.1, P9.2, P9.3)|
|7||14 March, 2017||MM||Nearest Neighbor, Bayes and Naive Bayes||C10, C11. (P11.1, P11.2, P10.1)|
|8||21 March, 2017||MM||Artificial Neural Networks and Bias/Variance||C12, C13. (P13.1, P13.2, P13.3)|
|9||28 March, 2017||MM||AUC and ensemble methods||C14, C15. (P14.1, P14.2, P15.1)|
|Unsupervised learning: Clustering and density estimation|
|10||4 April, 2017||MM||K-means and hierarchical clustering (Hand in project 2 before 13:00)||C16. (P16.1, P16.2, P16.3)|
|11||18 April, 2017||MM||Mixture models and density estimation||C17, C18. (P18.1, P17.1, P17.2)|
|12||25 April, 2017||MM||Association mining||C19. (P19.1, P16.2, P16.3)|
|13||2 May, 2017||MM||Recap and discussion of the exam (Hand in project 3 before 13:00)||C1-C19|
(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.)