DAT300 Applied Deep Learning
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Showing course contents for the educational year 2022 - 2023 .
Course responsible: Fadi al Machot
Teachers: Oliver Tomic
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
(NO=norsk, EN=Engelsk)
(NO=norsk, EN=Engelsk)
Teaching exam periods:
The starts in the autumn parallel.
The course will be taught / censored in the autumn parallel.
Course frequency: Annually
First time: Study year 2018-2019
Course contents:
DAT300 builds upon subjects students have learned in DAT200 - Applied Machine Learning. The covered methodology may include:
- Foundations of artificial neural networks (NN)
- Deep convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
- Basic processing and analysis of out-of-core data sets
The course will introduce the theoretical basics of the methods discussed and focus heavily on applications and modelling with real data. The students will learn to build effective and accurate models that, depending on the application, can contribute to several of UN's sustainability goals, among others 3, 11, 12, 14, 15.
Learning outcome:
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.
Learning activities:
The course will consist of lectures and practical exercises using computers and modern machine learning / deep learning software (with help from teaching assistants).
Teaching support:
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.
Syllabus:
Curriculum, programs, supporting literature, etc. will be announced on the course web page.
Prerequisites:
DAT200 or similar.
INF200 or a similar course in advanced programming.
Recommended prerequisites:
MATH280
Mandatory activity:
Compulsory hand-in assignments. Rules for approving obligatory activities will be announced when the course starts.
Assessment:
Written exam, 3.5 hours. A-F.
Nominal workload:
Lectures: 78 hours. Exercises: 26 hours. Colloquia and self study: 146 hours
Type of course:
Lectures: 4 hours per week. Exercises: 2 hours per week.
Examiner:
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: Written exam: Letter grades