DAT300 Applied Machine Learning II
There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.
Showing course contents for the educational year 2019 - 2020 .
Course responsible: Oliver Tomic
Teachers: Kristian Hovde Liland
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
Teaching exam periods:
The starts in the autumn parallel.
The course will be taught / censored in the autumn parallel.
Course frequency: Annually
First time: 2018H
DAT300 builds upon subjects students have learned in DAT200 - Applied Machine Learning. The covered methodology may include:
- Feature engineering techniques for machine learning
- Foundations of artificial neural networks (NN)
- Deep convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
- Processing and analysis of large data sets
The course will introduce the theoretical basics of the methods discussed and focus heavily on applications and modelling with real data.
Good skills and insight into basic techniques for machine learning and deep learning. Basic understanding of various models' mathematical properties and operations. The student will learn to master analysis methods suited for 1) General purpose Machine Learning, 2) Sequence analysis, 3) image classification. The student will learn to connect problem characteristics with choice of appropriate learning algorithms.
The course will consist of lectures and practical exercises using computers and modern machine learning / deep learning software (with help from teaching assistants).
Machine learning / deep learning is a subject that constantly evolves, and online learning resources will be connected to lectures and exercises through the course webpages in Canvas.
The students can also request appointments with the lecturer in his/her office on pre-arranged times and via email.
Curriculum, programs, supporting literature, etc. will be announced on the course web page.
DAT200 or similar.
INF200 or a similar course in advanced programming.
Compulsory hand-in assignments. Rules for approving obligatory activities will be announced when the course starts.
Written exam, 3.5 hours.
Lectures: 78 hours. Exercises: 26 hours. Colloquia and self study: 196 hours
Type of course:
Lectures: 4 hours per week. Exercises: 2 hours per week.
An external censor will participate together with the internal censor in forming the exam and censor guide. The external censor checks the internal censor's assessment of a random selection of candidates as a calibration at certain intervals in line with the faculty's guidelines for censoring.
Allowed examination aids: A1 No calculator, no other aids
Examination details: One written exam: A - E / F