SOW-BKI230A
Deep Learning
Course infoSchedule
Course moduleSOW-BKI230A
Credits (ECTS)6
CategoryB2 (Second year bachelor)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
Lecturer(s)
Coordinator
dr. U. Güçlü
Other course modules lecturer
Contactperson for the course
dr. U. Güçlü
Other course modules lecturer
Examiner
dr. U. Güçlü
Other course modules lecturer
Academic year2021
Period
SEM2  (31/01/2022 to 15/07/2022)
Starting block
SEM2
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
After successful completion of this course, you will be able to:
  • Understand basics and preliminaries of deep learning
  • Understand modern deep learning techniques
  • Understand scalability, efficiency and applications of deep learning
  • Implement these techniques in Python/MXNet/Jupyter.
Content
In this course, you will learn about the concepts, the context and the code all the way from the humble beginnings of deep learning to the recent deep learning revolution that has completely transformed artificial intelligence from science fiction to reality as you study topics such as:
  • Linear neural networks and multilayer perceptrons
  • Deep learning computation, (modern) convolutional neural networks, (modern) recurrent neural networks and attention mechanisms
  • Optimization algorithms, computational performance and computer vision
by following the open source book Dive into Deep Learning in weekly lectures and working on practical assignments in weekly labs.
Level
AI-B2
Presumed foreknowledge
  • SOW-BKI124 Linear Algebra 
  • SOW-BKI132 Programming 2
  • SOW-BKI137 Probability Theory
Test information
  • Exam: 100% of the final grade (one retake)
Specifics

Instructional modes
Lecture
Attendance MandatoryYes

Supervised computer practicals
Attendance MandatoryYes

Tests
Exam
Test weight1
Test typeExam
OpportunitiesBlock SEM2, Block SEM2