MS Artificial
Intelligence
Department
Computer Science
Level
Graduate
College
College of Computer Sciences & Information Systems
Course Detail
The MS in Artificial Intelligence program offers students a strong base of essential skills and knowledge that are vital for adapting to the swift shifts occurring in this dynamic field. This AI degree curriculum lays the groundwork and advanced proficiencies in the fundamental principles and technologies underpinning AI. These include logic, knowledge representation, probabilistic models, and machine learning. Students have the opportunity to delve into specific areas through specialized courses, such as computer vision, remote sensing, and natural language processing. Artificial Intelligence stands as a rapidly evolving and demanding domain, poised to be a major driving force across various industries in the immediate future. The transformative impacts it introduces are becoming increasingly evident, whether it’s in the functionality of smartphones or the imminent realization of self-driving vehicles.
The program is designed in accordance with the Higher Education Commission (HEC) guidelines. It spans a minimum duration of two years, comprising at least four semesters, and requires the successful completion of 30 credit hours. To meet the degree requirements, students must complete eight taught courses along with the 6 credit hours of research thesis (for MS by Thesis) or 6 credit hours of Independent Research Studies (IRS – I and IRS – II for MS by Independent Research Study) or 6 credit hours of 2 courses (Elective IV and Elective V where each course is of 3 credit hours for MS by Coursework). The MS by Coursework is subject to the approval of the departmental committee.
Eligibility
To be eligible for the MS in Artificial Intelligence (MSAI) program, candidate must have a 4-year Bachelor’s degree (16 years of education) in a relevant computing discipline (such as BS Artificial Intelligence, BS Computer Science, BS IT, etc.) from an HEC-recognized university, with a minimum 2.5 CGPA on a 4.0 scale or a 2nd Division).
MS Artificial Intelligence students learn to:
- Proficiency in advanced coding using a high-level programming language (such as Python or C++).
- Application of coding expertise, creative thinking, and design skills to construct state-of-the-art AI systems.
- Transformation of abstract AI challenges into specific project requirements.
- Familiarity with machine learning, natural language processing, and computer vision mechanisms, algorithms, and contemporary architectures.
- Identification of precise performance metrics (such as AI system sensitivity and specificity).
- Execution of simulations/experiments to validate and enhance software performance.
- Creation and implementation of scalable software architectures, components, and APIs (Application Programming Interfaces).
- Optional achievement: Capability to formulate and defend an AI-focused MS thesis, involving problem definition, literature review, method development, testing, and result analysis.
- Optional accomplishment: Conduct of independent AI research, refining the aforementioned skills, and creation of a detailed report outlining the work.
Learning Outcomes for MS Artificial Intelligence students include:
- Be able to exhibit a profound comprehension of advanced artificial intelligence and machine learning practices, encompassing conceptualization, analysis, design, verification, and deployment.
- Be able to adeptly address intricate challenges in machine learning and artificial intelligence, leveraging contemporary principles, algorithms, technologies, methodologies, and tools.
- Be able to assume leadership roles and actively contribute within collaborative teams dedicated to the development of AI and machine learning applications.
- Be able to maintain a conscious awareness of the ethical, economic, and environmental implications associated with their work, as context demands.
- Be able to achieve ongoing success in their chosen profession while embracing a commitment to continual learning in engineering or other relevant professional spheres.
- Be able to effectively and persuasively communicate with a diverse range of audiences, employing adept communication skills.
Career Path:
1. Machine Learning Engineer | 2. Data Scientist | 3. Computer Vision Engineer |
4. AI in Gaming Developer | 5. AI Research Scientist | 6. AI Ethics Consultant |
7. AI Product Manager | 8. Robotics Engineer | 9. AI in Healthcare Specialist |
10. AI Solutions Architect | 11. AI Consultant | 12. AI in Finance Specialist |
Prospective Employers:
1. Technology Companies | 2. Research Institutions | 3. Automotive Industry |
4. Healthcare Industry | 5. Financial Services | 6. E-commerce and Retail |
7. Manufacturing and Logistics | 8. Gaming Industry | 9. Aerospace and Defense |
Course Structure (Semester Wise)
Semester 1
Course Title | Credit hours |
Research Methodology | 3 |
Advanced Artificial Intelligence | 3 |
Advanced Machine Learning | 3 |
Understanding of Holy Quran I * | 1 |
Total: | 10* |
Semester 2
Course Title | Credit hours |
Advanced Deep Learning | 3 |
Information Retrieval Techniques | 3 |
Elective I | 3 |
Understanding of Holy Quran II * | 1 |
Total: | 10* |
Semester 3
Course Title | Credit hours |
Elective II | 3 |
Elective III | 3 |
Total: | 6 |
Semester 4
Course Title | Credit hours |
MS Thesis /IRS – I and IRS – II / Elective IV** and Elective V ** | 6 |
Total: | 6 |
* Muslim students are required to take Understanding of the Holy Quran I (1 credit hour) and Understanding of the Holy Quran II (1 credit hour) to complete the degree requirement as per HEC requirement.
**For MS by Coursework, students are required to take 2 additional courses (Elective IV and Elective V) each of 3 credit hours (subject to the approval of the departmental committee).
Core Courses:
Courses | Credit Hours |
Advanced Artificial Intelligence | 3+0 |
Advanced Machine Learning | 3+0 |
Advanced Deep learning | 3+0 |
Information Retrieval Techniques | 3+0 |
Research Methodology | 3+0 |
Elective Courses:
Courses | Credit Hours |
Evolutionary Computing | 3+0 |
Image Processing | 3+0 |
Knowledge Engineering | 3+0 |
Parallel Algorithms | 3+0 |
Information Theory | 3+0 |
Artificial Intelligence in Cryptography | 3+0 |
Computer Vision: From Theory to Applications | 3+0 |
Artificial Neural Networks | 3+0 |
Medical Image Processing and Analysis | 3+0 |
Knowledge Graphs for Explainable Artificial Intelligence | 3+0 |
Intelligent Video Analytics | 3+0 |
Artificial Intelligence in Sports Analytics | 3+0 |
Artificial Intelligence in Automation | 3+0 |
Automated Reasoning | 3+0 |
Pattern Classification and Recognition | 3+0 |
Ubiquitous Computing and Intelligent Systems | 3+0 |
Internet of Things and Sensor Networks | 3+0 |
Design of Intelligent Information Systems | 3+0 |
Artificial Intelligence in Secure Network Analysis | 3+0 |
Social Network Analysis | 3+0 |
Web Mining | 3+0 |
Artificial Intelligence in Information Security | 3+0 |
Brain-Computer Interface | 3+0 |
Advanced Natural Language Processing | 3+0 |
Statistical Relational Artificial Intelligence | 3+0 |
MS Thesis | 6+0 |
Independent Research Study-I | 3+0 |
Independent Research Study-II | 3+0 |