Program Overview

In a world increasingly shaped by data and intelligent systems, Artificial Intelligence stands out as a transformative field that blends technology with human-like thinking. The MS Artificial Intelligence MS (AI) program prepares students to extract valuable insights from data and intelligence systems in a state-of-the-art manner. The program is designed for students who want to begin or advance their careers in artificial intelligence. It offers a blend of mathematics behind AI, machine learning, deep learning, computer vision, natural language processing, reinforcement learning, management of massive data sets, data visualization, and AI Ethics.

The program is open for undergraduate disciplines from national (HEC recognized) and international universities and thus preparing students for artificial intelligence applications in a wide range of domains. The core courses develop a strong mathematical, statistical, artificial intelligence, and machine learning foundation for the students. Students build on this foundation through a diversity of available electives, notably deep learning, text analytics, computer vision, reinforcement learning, and social network analysis.

The potential of MS (AI) in imparting useful advanced computing skills and professional growth is measured by the readiness of the job market and advanced learning schools in absorbing graduates. The curriculum design ensures that the graduates can creatively find technology-based solutions, think critically, and analyze systems and emerging problems independently.

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Eligibility Criteria

The following criteria will be strictly followed for admission to this Master program:

1. Applicant must have a minimum 16 years of education in the relevant subject of Science (such as Computer Science, Statistics, Economics, Mathematics, Accounts & Finance, Physics, etc.) and Engineering (Electrical Engineering, Electronics Engineering, etc.) with a minimum CGPA of 2.5 out of 4 (in a semester-based system), or 60% marks (in annual system) in their last degree.

2. Minimum 600 score in GMAT/160 in Quantitative, and 150 in the Verbal section of GRE required for test exemption.

3. Admission to MS (Data Science) program, as with all other programs at IBA, will be carried out strictly on merit. There are no reserve seats for any category.

4. Upon admission, candidates will be evaluated based upon their academic performance and their scores in the admission test and in the interview.

5. All equivalency claims for any previous achievement shall be evaluated by HEC.

6. There are two written admission tests: English and Mathematics for students coming from non-CS background while three written admission tests: English, Mathematics and Computer Science for students coming from CS/IT background. The Mathematics section would be primarily from Probability & Statistics, Linear Algebra and Calculus.

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Program Educational Objectives (PEOs) *

* [Source: NCAI, Curriculum for MS (AI)]

PEO1: Demonstrate a strong competence in Artificial Intelligence, resulting in successful careers.

PEO2: Pursuing research and innovation in Artificial Intelligence and be able to provide modern AI solutions to technical problems.

PEO3: To apply as well as create Artificial Intelligence based knowledge at par with the developments at both national and international levels.

Program Learning Objectives (PLOs)

PLO1: Develop a competitive blend of theoretical and practical AI-based skills, centered on statistics, probability, linear algebra, optimization, machine learning and all prominent dimensions of artificial intelligence.

PLO2: Develop a unique mindset of solving AI-related problems and analytical thinking, due to the severely practical and comprehensive conduct of courses.

PLO3: Prepare students to bring a revolutionary change by initiating and enhancing artificial intelligence initiatives in their respective corporate sectors by employing the skills and knowledge acquired in this program.

PLO4: Facilitate job promotion for students, from mid-level IT/analytics positions to senior-level positions by adding to their skills and academic qualifications.

PLO5: Engage students with qualified faculty of international recognition and encourage them to undertake research that may potentially lead to doctoral work in the domain of AI.

PLO6: Recognize and evaluate ethical, legal, and societal issues in AI systems, including fairness, accountability, transparency, and bias.

Scheme of Studies

  • Degree Program

    TITLE OF DEGREE PROGRAM

    Master of Science in Artificial Intelligence

  • Duration

    DURATION OF COURSES

    Four semester program with 30 credit
    hours with/without thesis

  • Foundation Courses

    FOUNDATION COURSES

    Data Structures & Algorithms | Databases
    *courses are required if not studied at undergraduate level

Core Courses Catalog

Machine
Learning

This course builds on the foundation of the theory related to Machine Learning in general, and its subbranch Supervised Learning. ML, sometimes labeled as data science, is the process of learning mathematical models related to data. The idea is to approximate the data with models, and use the models to perform useful tasks, e.g., predictions in the case of supervised learning. In doing so, the model automatically improves its performance over time. The course focuses on covering algorithmic theory and investigative practical experiments to inculcate problem solving skills and knowledge related to predictive modeling. Advanced topics, e.g., addressing the class imbalance problem, will also be covered.

Mathematical
Foundations of AI

This course provides the essential mathematical tools and theoretical underpinnings required for advanced study and research in Artificial Intelligence. Students will explore the core mathematical structures—linear algebra, probability theory, statistics, calculus, and optimization—that form the computational backbone of modern AI algorithms. On the theoretical side, the course examines computational complexity, algorithmic efficiency, logic and reasoning systems, and learning theory, establishing the limits and capabilities of AI models

Deep
Learning

Deep Learning have achieved state of the art performance on several compute vision, natural language and speech recognition benchmarks. Deep learning algorithms extract layered high and low-level features from raw data. With increasing non-line hidden layers, the discriminative power of the network improves. This course builds on the fundamentals of Neural networks and artificial intelligence and covers advanced topics in neural networks, convolutional and recurrent network structures, transformers and deep unsupervised learning. It also embeds applications of these algorithms to several real-world problem in computer vision, speech recognition, natural language processing, pattern recognition, etc.

Multimodal Large Language Models

This course examines the principles, architectures, and applications of multimodal large language models (MLLMs)—AI systems capable of processing and generating information across text, images, audio, video, and other modalities. Students will study the underlying components that enable multimodal understanding, including modality-specific encoders, cross-modal fusion mechanisms, attention architectures, and unified embedding spaces. The course integrates theoretical concepts with practical skills in model fine-tuning, evaluation, and deployment for tasks such as visual question answering, text-to-image generation, and multimodal reasoning. Ethical considerations, bias mitigation, and real-world case studies are also addressed to prepare students for research and industry roles in next-generation AI systems.

GENERAL INFORMATION

CURRICULUM STRUCTURE

Duration

2 Years

Semesters

4

Courses

4

Electives

6

Thesis

2

Project

1

* One elective from another School/Department is allowed provided the elective is approved by Graduate Program Director.
* Minimum 18 hours are required to opt for MS Thesis with CGPA >=3.0

AI Elective Courses

Course Title

Credit Hours

NLP with Deep Learning

3

Computer Vision

3

Computational Intelligence

3

Probabilistic Reasoning

3

Social Network Analysis

3

Business Intelligence

3

Machine Learning-II (Unsupervised Learning)

3

Generative AI

3

Reinforcement Learning

3

Agentic AI

3

Cloud Computing

3

 

SEMESTER-WISE SEQUENCE OF COURSES

Semester 1
Course Title Credit Hours
Mathematical Foundations of AI 3+0
Machine Learning 3+0
Deep Learning 3+0
Semester 2
Course Title Credit Hours
Multimodal LLMs 3+0
Elective I 3+0
Elective II 3+0
Semester 3
Course Title Credit Hours
Elective III 3+0
Thesis I / Elective IV 3+0
Semester 4
Course Title Credit Hours
Elective V 3+0
Thesis II / Project / Elective VI 3+0