TEK9040 – Deep Learning for Autonomous Systems

Schedule, syllabus and examination date

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Course content

The course focuses on  advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep learning techniques can be used to design important parts of advanced autonomous systems that exist in physical and cyber environments.

Learning outcome

After completing the course

  • you have an overview of modern algorithms and architectures for deep learning with neural networks relevant to autonomous systems
  • you have a thorough knowledge of recurrent neural networks and their extensions with memory and attention
  • you have a knowledge of selected advanced algorithms in deep reinforcement learning
  • you are familiar with stochastic approaches to deep learning and unsupervised learning
  • you know how autonomous systems can benefit from deep learning for understanding and decision making
  • you know how modern tools, such as TensorFlow, are used to create important components of advanced autonomous systems

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

It is recommended that you have a good prior knowledge of linear algebra, statistics and calculus, from for example MAT1110 – Calculus and Linear Algebra / MAT1120 – Linear AlgebraSTK1100 – Probability and Statistical Modelling.

The course builds on basic knowledge of machine learning and neural networks, for example from courses IN3050 – Introduction to Artificial Intelligence and Machine Learning / IN4050 – Introduction to Artificial Intelligence and Machine Learning or IN5400 – Machine Learning for Image Analysis.

Overlapping courses

Teaching

The course provides 3 hours of lectures + 2 hours of group lessons per week throughout the semester.

There will be 3 compulsory practical exercises and one student presentation, which must be approved in order to take the final exam. As a PhD candidate you will in addition write an essay, which must be approved in order to take the final exam. 

Examination

  • There will be a final written examination, which counts 100% towards the final grade.

All compulsory exercises and other compulsory activities must be approved in order to take the exam.

It will also be counted as 1 of the 3 attempts to sit the exam for this course, if you sit the exam for the following course:

Examination support material

No examination support material is allowed.

Language of examination

Courses taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass, the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Sep. 28, 2022 11:24:14 AM

Facts about this course

Credits
10
Level
PhD
Teaching
Autumn
Examination
Autumn
Teaching language
Norwegian (English on request)