BSc: Introduction To Computer Vision
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Contents
--------


* [1 Introduction to Computer Vision](#Introduction_to_Computer_Vision)
	+ [1.1 Short Description](#Short_Description)
	+ [1.2 Prerequisites](#Prerequisites)
		- [1.2.1 Prerequisite subjects](#Prerequisite_subjects)
		- [1.2.2 Prerequisite topics](#Prerequisite_topics)
	+ [1.3 Course Topics](#Course_Topics)
	+ [1.4 Intended Learning Outcomes (ILOs)](#Intended_Learning_Outcomes_.28ILOs.29)
		- [1.4.1 What is the main purpose of this course?](#What_is_the_main_purpose_of_this_course.3F)
		- [1.4.2 ILOs defined at three levels](#ILOs_defined_at_three_levels)
			* [1.4.2.1 Level 1: What concepts should a student know/remember/explain?](#Level_1:_What_concepts_should_a_student_know.2Fremember.2Fexplain.3F)
			* [1.4.2.2 Level 2: What basic practical skills should a student be able to perform?](#Level_2:_What_basic_practical_skills_should_a_student_be_able_to_perform.3F)
			* [1.4.2.3 Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?](#Level_3:_What_complex_comprehensive_skills_should_a_student_be_able_to_apply_in_real-life_scenarios.3F)
	+ [1.5 Grading](#Grading)
		- [1.5.1 Course grading range](#Course_grading_range)
		- [1.5.2 Course activities and grading breakdown](#Course_activities_and_grading_breakdown)
		- [1.5.3 Recommendations for students on how to succeed in the course](#Recommendations_for_students_on_how_to_succeed_in_the_course)
	+ [1.6 Resources, literature and reference materials](#Resources.2C_literature_and_reference_materials)
		- [1.6.1 Open access resources](#Open_access_resources)
		- [1.6.2 Closed access resources](#Closed_access_resources)
		- [1.6.3 Software and tools used within the course](#Software_and_tools_used_within_the_course)
* [2 Teaching Methodology: Methods, techniques, & activities](#Teaching_Methodology:_Methods.2C_techniques.2C_.26_activities)



Introduction to Computer Vision
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* **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.



Prerequisites
-------------


### Prerequisite subjects


### Prerequisite topics


Course Topics
-------------




Course Sections and Topics
| Section | Topics within the section
 |
| --- | --- |
| Representation of images and videos | 1. Computer representation
2. Rescaling/manipulating images
 |
| Image Classification | 1. Loss Functions
2. Backpropagation
3. Neural Networks
4. Training
 |
| Convolutional Neural Networks | 1. Training
2. Architectures
 |
| Recurrent Neural Networks | 1. Training
2. Architectures
 |
| Image Segmentation and object detection | 1. 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
 |


### Recommendations for students on how to succeed in the course


Resources, literature and reference materials
---------------------------------------------


### Open access resources


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


### Closed access resources


### Software and tools used within the course


Teaching Methodology: Methods, techniques, & activities
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