BSc: Introduction To Computer Vision

Introduction to Computer Vision

• Course name: Introduction to Computer Vision
• Code discipline: XXX
• Subject area:

Short Description

This course covers the following concepts: Computer vision using machine learning models.

Course Topics

Course Sections and Topics

Section	Topics within the section
Representation of images and videos	Computer representation Rescaling/manipulating images
Image Classification	Loss Functions Backpropagation Neural Networks Training
Convolutional Neural Networks	Training Architectures
Recurrent Neural Networks	Training Architectures
Image Segmentation and object detection	Techniques

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

• Significant exposure to real-world implementations
• To develop research interest in the theory and application of computer vision

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

• Suitability of different computer vision models in different scenarios
• Ability to choose the right model for the given task

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

• Hands on experience to implement different models to know inside behavior
• Sufficient exposure to train and deploy model for the given task
• Fine tune the deployed model in the real-world settings

Grading

Course grading range

Grade	Range	Description of performance
A. Excellent	91-100	-
B. Good	78-90	-
C. Satisfactory	60-77	-
D. Poor	0-59	-

Course activities and grading breakdown

Activity Type	Percentage of the overall course grade
Weekly Labs	50
Weekly Quizzes	10
Midterm Exam	15
Final Exam	25

Resources, literature and reference materials

Open access resources

• Handouts supplied by the instructor
• Materials from the internet and research papers shared by instructor