TEK5040 – Deep Learning for Autonomous Systems
Course description
Schedule, syllabus and examination date
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
Students admitted at UiO must?apply for courses?in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
Nordic citizens and applicants residing in the Nordic countries may?apply to take this course as a single course student.
If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures for international applicants.
Recommended previous knowledge
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 Algebra, STK1100 – 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 (continued).
Overlapping courses
- 10 credits overlap with TEK9040 – Deep Learning for Autonomous Systems.
Teaching
The teaching?includes?3 hours of lectures and 2 hours of group teaching?per week throughout the semester.
This course has?3 mandatory?practical exercises and one student presentation, which must be approved before you can sit?the final exam.
Examination
- A final written exam counts 100% towards the final grade.
This course has?mandatory exercises that must be approved before you can sit the final 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
All printed and handwritten examination support materials are allowed, as well as an approved calculator.
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 scale from A to F, where A is the best grade and F?is a fail. 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.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.