Course Description

Deep Learning course with application on Computer Vision, using the PyTorch Library.

Academic Year 2023-2024

The course will start on January (exact date will be updated in the near future).

The course is intended for

Course objectives and outcomes

Objectives: to provide students with the fundamental elements of deep learning, demonstrate its application to computer vision, and form practical skills in implementing and using deep-learning-based systems.

Outcome: An understanding of fundamental concepts and methods of deep learning and its applications, focusing on computer vision, paired with a set of tools and skills required for realizing functioning deep-learning systems.

Required skills

The course will also give the basis to understand the fundamental concepts of machine learning, so knowing them is recommended but optional.

Course Outline

  1. Introduction to Machine Learning and Deep Learning (1 hour)
  2. Machine Learning Foundations (3 hours)
  3. Data Representation with Tensors (3 hours)
  4. Learning from Tensors: Gradient Descent and Backpropagation (4 hours)
  5. Designing and Improving Deep-learning Models for classification (3 hours)
  6. Real-Time Object Detection with YOLO (3 hours)
  7. Running Scientific Experiments with PyTorch (3 hours)


For MSc Students

The students can decide to take the exam in one of the following two formats (either one or the other):

For PhD Students

Register to this Teams group to get notifications about the course.


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