Course - Deep Learning - IT3030
Deep Learning
About
About the course
Course content
The course is a follow-up to TDT4173 Machine Learning. It gives thorough coverage of deep learning. The course covers both mathematical and computational foundation for deep learning, practical applications such as processing of images, text, and other modalities. Modern software frameworks for deep learning will be introduced and used for some projects, while other projects will require relatively low-level coding in Python or similar languages.
Learning outcome
Knowledge: General principles for learning/adaptive systems Mathematical and computational foundation for deep learning How to use deep learning in diverse practical applications Skills: Analyze different frameworks for deep learning in specific application domains Ability to analyze the mathematical foundation for diverse deep learning published in the literature Build computational systems that achieve deep learning General competences: Understand deep learning's basis in mathematics and cognitive science Understand possibilities and limitations of deep learning in practical settings
Learning methods and activities
Lectures, self study
Further on evaluation
Grades are based on a combination of project(s) (50%) and a single mid-term exam (50%). Results for each part are given as percentages, while the final grade is in letter form. Note that there is no re-sit exam in this course.
Specific conditions
Admission to a programme of study is required:
Computer Science (MIDT)
Computer Science (MTDT)
Industrial Economics and Technology Management (MTIØT)
Informatics (MIT)
Informatics (MSIT)
Required previous knowledge
TMA4115 Mathematics 3, TDT4120 Algorithms and data-structures, TDT4171 Methods in Artificial Intelligence, and TDT4173 Machine learning.
Course materials
Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning (2016). MIT Press. Supplementary articles will be handed out as needed.
Subject areas
- Computer Science
- Computer Systems