The Master of Artificial Intelligence program (Course-based) is thoughtfully designed to meet the needs of individuals seeking rewarding job prospects and working professionals aiming to advance their qualifications and elevate their careers in the fast-evolving field of artificial intelligence. This study plan has been prepared to deliver coherent and sequential knowledge through a state-of-the-are curriculum to the students. Hence, students shall be able to conduct high quality research and make contribution to meet the VISION 2030 of the kingdom.
Vision
Department of computer science envisages to be pioneer in providing world class education and research in the frontier areas of computer science and its applications.
Mission
To provide a high-quality program for Artificial Intelligence aspirants that meets the labor market demands, research trends, and innovation, and contributes to the development of society and industry for achieving the national goals.
Goals
Provide competitive scientific and technical educational environment targeting towards professional excellence.
Encourage fundamental and applied research in fulfilling the professional needs of the computing industry.
Program Objectives
Independently solve business problems using Artificial Intelligence.
Pursue academic excellence through advanced study and continued research leading to professional development.
Demonstrate active engagement and leadership to promote professional and organizational goals that address the needs of the community.
Program Learning Outcomes
Demonstrate an understanding of mathematical concepts, algorithmic principles, and artificial intelligence theoretical knowledge.
Analyze and assess emerging technological developments applied to artificial intelligence.
Design, develop, and optimize Artificial Intelligence algorithms for real-world problems.
Explore and identify learning and technical resources needed to adopt an AI strategy
Present innovative architectural AI designs through strong communication skills.
Demonstrate professional ethics and governance, to ensure successful AI project delivery
Lead informed, strategic decision-making and augment business performance by integrating key AI management and leadership insights with societal concerns.
Graduate Attributes
Competitive Knowledge: High technical competencies to identify, design, implement and evaluate computing-based solutions with recent techniques
Critical Thinking: Analytical and critical spirit to participate in the implementation of innovative computing solutions.
Exploratory Research: Scientific rigor to carry out state of the art research to investigate problems and derive innovative solution models.
Effective communication: Communicate effectively to differed scientific level of audience in written and oral and to plan and manage collaborative research.
Social Responsibility: Sense of personal and social responsibility, following ethical and professional standards, leading to active participation and contribution to research society.
Adoptability: Adaptable to work environment with self-confidence and flexible nature.
Self-Learning: Capable to generate extensive knowledge through continued self learning.
Career Opportunities
AI Engineer
Machine Learning Engineer/ Expert
Computer vision Engineer
User Experience Design Engineer
AI Research Scientist
Lecturer/ Research Assistant
Main Tracks
Machine Learning.
Computer Vision.
Program Duration
The standard number of years in which a student can complete the program is 2 years (4 Semesters).
Study Plan (33 Credit Hours)
|
| Year 1 |
| ||
| Semester I |
|
| Semester II |
|
Code | Course Name | CH | Code | Course Name | CH |
CS 6211 | AI Foundations | 3 | CS 6221 | Deep Learning | 3 |
CS 6111 | Mathematical Methods for Computing | 3 | CS 6213 | Feature Engineering and Model Selection | 3 |
CS 6212 | Advanced Machine Learning | 3 | CS 6231 | Computer Vision | 3 |
9 | 9 | ||||
|
| Year 2 |
| ||
| Semester III |
|
| Semester IV |
|
Code | Course Name | CH | Code | Course Name | CH |
CS 6222 | Statistical Learning | 3 | *** | Elective 2 | 3 |
*** | Elective 1 | 3 | CS 6241 | Capstone Project | 3 |
CS 6214 | Time-Series Modeling | 3 |
|
|
|
9 | 6 | ||||
| Tracks Electives |
| |||
Track 1: | Machine Learning |
| Track 2: | Computer Vision |
|
Code | Course Name | CH | Code | Course Name | CH |
CS 6223 | Reinforcement Learning | 3 | CS 6232 | 3D Imaging and Analysis | 3 |
CS 6224 | Natural Language Processing | 3 | CS 6233 | Deep Learning in Computer Vision | 3 |
|
| ||||