MS Artificial
Intelligence
Department
Computer Science
Level
Graduate
College
College of Computer Sciences & Information Systems
Program 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 is in the functionality of smartphones or the imminent realization of self-driving vehicles.
The program is designed in accordance with Higher Education Commission (HEC) guidelines. It spans a minimum of 2 years, comprising at least 4 semesters, and requires the successful completion of 32 credit hours.
The following Program Objectives are articulated for the MS Artificial Intelligence program:
- Develop advanced programming and design proficiency to conceptualize, implement, and scale state-of-the-art artificial intelligence systems using high-level programming languages, software architectures, components, and application programming interfaces.
- Apply machine learning, natural language processing, computer vision, knowledge representation, probabilistic models, and contemporary AI architectures to translate abstract AI challenges into specific project requirements and measurable performance criteria.
- Conduct simulations and experiments, apply appropriate performance metrics, and systematically validate, evaluate, and improve the performance of AI-based software solutions.
- Develop independent research capability through problem formulation, literature review, method development, experimentation, testing, result analysis, thesis preparation, and professional reporting in artificial intelligence.
Learning Outcomes for MS Artificial Intelligence students include:
- Demonstrate a profound understanding of advanced artificial intelligence and machine learning practices, encompassing conceptualization, analysis, design, verification, and deployment.
- Address complex challenges in machine learning and artificial intelligence by applying contemporary principles, algorithms, technologies, methodologies, and tools.
- Assume leadership roles and contribute effectively within collaborative teams engaged in the development of artificial intelligence and machine learning applications.
- Demonstrate awareness of the ethical, economic, social, and environmental implications associated with artificial intelligence systems and professional practice.
- Achieve continued professional success while maintaining a commitment to lifelong learning in artificial intelligence, engineering, and related professional domains.
- Communicate technical concepts, methods, findings, and recommendations effectively and persuasively to diverse audiences.
Eligibility
To be eligible for the MS in Artificial Intelligence (MSAI) program, candidates must hold a four-year Bachelor’s degree (16 years of education) in a relevant computing discipline, such as BS Artificial Intelligence, BS Computer Science, or BS Information Technology, from an HEC-recognized university, with a minimum CGPA of 2.5 on a 4.0 scale or Second Division. If the student is coming from irrelevant background, then the student must pass 4 deficiency courses
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.
Career Path:
1. | Machine Learning Engineer | 2. | Data Scientist | 3. | Data Analyst/Engineer |
4. | Business Intelligence Analyst | 5. | Big Data Engineer | 6. | Computer Vision Engineer |
7. | Data Product Manager | 8. | Data Mining Engineer | 9. | Healthcare Data Scientist |
10. | Data Architect | 11. | Operations Research Analyst | 12. | Data Science Consultant |
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. | Educational Institutions | 9. | Aerospace and Defense |
Program Requirement:
To fulfill the degree requirements, students must complete eight taught courses along with 6 credit hours of research thesis for MS by Thesis, or 6 credit hours of Independent Research Studies, namely IRS-I and IRS-II, for MS by Independent Research Study, or 6 credit hours through two courses, Elective IV and Elective V, each carrying 3 credit hours, for MS by Coursework. The MS by Coursework option shall be subject to the approval of the departmental committee. For MS Thesis requirement, the students must refer to the MS Policy Manual available at https://basr.iobm.edu.pk/ms-mphil-and-phd-policy-and-thesis/.
Course Structure (Semester Wise)
Semester One | Semester Two |
Research Methodology | Advanced Deep Learning |
Semester Three | Semester Four |
Semester Three IRS-1/ Elective IV */ MS Thesis-I Understanding of Holy Quran-I **/ Comparative Religion-I ** | Semester Four IRS – II / and Elective V * / MS Thesis-II Understanding of Holy Quran-II **/ Comparative Religion-II ** |
* Subject to the approval of the departmental committee.
** Muslim students are required to take Understanding of Holy Quran-I (1 Credit hour) and Understanding of the Holy Quran-II (1 Credit hour) and Non-Muslim students are required to take Comparative Religion-I and Comparative Religion-II.
Core Courses:
Course Code | Course Title | Credit Hours |
CSP 511 | Research Methodology | 3+0 |
AIP 581 | Advanced Artificial Intelligence | 3+0 |
AIP 582 | Advanced Machine Learning | 3+0 |
AIP 583 | Advanced Deep Learning | 3+0 |
AIP 584 | Information Retrieval Techniques | 3+0 |
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Elective Courses:
Course Code | Course Title | Credit Hours | Course Code | Course Title | Credit Hours |
AIP 531 | Information Theory | 3+0 | AIP 532 | Automated Reasoning | 3+0 |
AIP 541 | Parallel Algorithms | 3+0 | AIP 542 | Ubiquitous Computing and Intelligent Systems | 3+0 |
AIP 571 | Internet of Things and Sensor Networks | 3+0 | AIP 585 | Artificial Neural Networks | 3+0 |
AIP 586 | Pattern Classification and Recognition | 3+0 | AIP 587 | Evolutionary Computing | 3+0 |
AIP 588 | Knowledge Engineering | 3+0 | AIP 589 | Knowledge Graphs for Explainable Artificial Intelligence | 3+0 |
AIP 681 | Statistical Relational Artificial Intelligence | 3+0 | AIP 682 | Design of Intelligent Information Systems | 3+0 |
AIP 683 | Image Processing | 3+0 | AIP 684 | Computer Vision: From Theory to Applications | 3+0 |
AIP 685 | Medical Image Processing and Analysis | 3+0 | AIP 686 | Intelligent Video Analytics | 3+0 |
AIP 687 | Brain-Computer Interface | 3+0 | AIP 688 | Advanced Natural Language Processing | 3+0 |
AIP 689 | Web Mining | 3+0 | AIP 512 | Social Network Analysis | 3+0 |
AIP 513 | Artificial Intelligence in Sports Analytics | 3+0 | AIP 514 | Artificial Intelligence in Automation | 3+0 |
AIP 515 | Artificial Intelligence in Cryptography | 3+0 | AIP 516 | Artificial Intelligence in Information Security | 3+0 |
AIP 517 | Artificial Intelligence in Secure Network Analysis | 3+0 |
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Note: Students must select at least three electives from the above list
AIP 691 | MS Thesis – I | 3+0 | AIP 692 | MS Thesis – II | 3+0 |
AIP 693 | Independent Research Study – I | 3+0 | AIP 694 | Independent Research Study – II | 3+0 |
Note: Students must choose either the MS Thesis, the Independent Research Study, or two elective courses from the Elective Courses List. If a student opts for the MS Thesis, enrollment in MS Thesis–II is mandatory to maintain continuous registration until the thesis is formally submitted to the Board of Advanced Studies and Research (BASR) for further processing.