BS Computational Mathematics and Artificial Intelligence

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BS Computational Mathematics and Artificial Intelligence

Program Overview

The BS in Computational Mathematics and Artificial Intelligence at National Skills University is an advanced interdisciplinary degree that brings together mathematical sciences, computing, and intelligent technologies to address the demands of a rapidly evolving digital world. The program is designed in line with HEC Undergraduate Education Policy and international academic standards, with a focus on developing strong analytical foundations in mathematics alongside practical competencies in programming, artificial intelligence, data-driven decision-making, and computational problem-solving. It prepares students to understand complex systems, build intelligent models, and apply algorithmic thinking to real-world challenges across diverse sectors such as industry, healthcare, finance, automation, and emerging technologies.

Eligibility

4 Years Study Plan

Program Study Plan / Scheme of Studies

1st Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	ENG-101	Functional English	General	3(2+1)
2	QR-101	Quantitative Reasoning-I	General	3
3	PST-101	Pakistan Studies	General	2
4	PHY-101	Physics	General	3(2+1)
5	ICT-101	Applications of Information and Communication Technologies (ICT)	General	3(2+1)
6	IST-101	Fehm-e-Quran / Understanding of Holy Quran-I	General	1(0+1)
7	CMA-101	Calculus and Analytical Geometry	CM	3
Total Credit Hours				18

2nd Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	GEN-101	Introduction to Arts and Humanities	General	2
2	ENG-102	Expository Writing	General	3
3	QR-102	Quantitative Reasoning-II	General	3
4	IST-102	Fehm-e-Quran / Understanding of Holy Quran-II	General	1(0+1)
5	CS-101	Fundamentals of Programming	Inter	3(2+1)
6	CMA-102	Multivariable Calculus	CM	3
7	MAT-103	Discrete Mathematics	Inter	3
Total Credit Hours				18

3rd Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	GEN-201	Introduction to Social Sciences	General	2
2	GEN-202	Ideology and Constitution of Pakistan	General	2
3	IST-201	Islamic Studies	General	2
4	CS-202	Advanced Programming	Inter	3(2+1)
5	AI-201	Artificial Intelligence	AI	3(2+1)
6	MAT-202	Elementary Differential Equations	CM	3
7	SE-201	Software Engineering	Inter	3(2+1)
Total Credit Hours				18

4th Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	GEN-203	Civics and Community Engagement	General	2
2	GR-225	Entrepreneurship	General	2
3	STA-201	Statistics & Probability	Inter	3(2+1)
4	MAT-204	Ordinary Differential Equations	CM	3
5	AI-202	Data Structures & Algorithms	AI	3(2+1)
6	AI-203	Database Systems	AI	3(2+1)
Total Credit Hours				16

5th Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	AI-301	Machine Learning	AI	3(2+1)
2	AI-302	Programming for Artificial Intelligence	AI	3(2+1)
3	CMA-301	Real and Complex Analysis	CM	3
4	AI-303	Digital Image Processing	AI	3(2+1)
5	CMA-302	Linear Algebra for Artificial Intelligence	CM	3(2+1)
6	MAT-304	Numerical Methods	CM	3(2+1)
Total Credit Hours				18

Internship: Internship can be undertaken either during the semester or in summer (3 Credit Hours).

6th Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	MAT-308	Functional Analysis-I	CM	3
2	STA-301	Computational Statistics	Inter	3
3	CMA-303	Optimization	CM	3(2+1)
4	AI-304	Knowledge Representation and Reasoning	AI	3
5	CMA-304	Stochastic Methods for Artificial Intelligence	CM	3
6	CMA-305	Numerical Linear Algebra	CM	3(2+1)
Total Credit Hours				18

7th Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	AI-401	Artificial Neural Networks and Deep Learning	AI	3(2+1)
2	AI-402	Natural Language Processing	AI	3(2+1)
3	CMA-401	Neural Computing for Differential Equations	CM	3(2+1)
4	-	Elective-I	Elective	3
5	-	Elective-II	Elective	3
Total Credit Hours				15

8th Semester

S. No.	Code	Course Title	Type	Cr. Hrs.
1	AI-403	Computer Vision	AI	3(2+1)
2	CMA-402	Graph Theory and Neural Networks	CM	3
3	-	Elective-III	Elective	3
4	-	Elective-IV	Elective	3
5	-	Capstone Project	Capstone	3
Total Credit Hours				15

Internship: Internship can be undertaken either during the semester or in summer (3 Credit Hours).

Admission Test

The admission test will be conducted at the University based on the Intermediate level. The distribution of marks is as follows:

Evaluation Category	Distribution of Marks
English	20%
Mathematics / Biology	60%
Analytical Skills	20%

Note: The Undergraduate Studies Aptitude Test (USAT) conducted by the Educational Testing Council (ETC) is acceptable in lieu of the NSU Admission Test.

The merit list of candidates will be determined by combining the weighted marks obtained in the entrance test with the weighted marks earned in the previous public examinations. The weightage criteria for undergraduate degree programs are as follows:

Evaluation Category	Distribution of Marks
Matric	10%
Intermediate	40%
Entrance Test	50%

Why Bachelor of Science in Computational Mathematics and Artificial Intelligence?

In today’s rapidly evolving digital world, artificial intelligence, data science, and computational technologies are transforming every sector of society. The BS Computational Mathematics and Artificial Intelligence program at National Skills University is designed to meet this demand by producing graduates who can connect mathematical theory with intelligent computing systems. It focuses on developing professionals who can design algorithms, build intelligent models, and use computational techniques to solve complex, real-world problems in areas such as automation, healthcare, finance, cybersecurity, and advanced engineering systems.

This degree emphasizes both strong mathematical foundations and practical skills in programming, machine learning, and AI-based systems development. Graduates are trained to become industry-ready professionals capable of working as AI engineers, data scientists, software developers, and computational researchers. The program also prepares students for advanced studies and innovation-driven careers, ensuring they can contribute meaningfully to technological advancement, digital transformation, and the growing global demand for intelligent and automated solutions.

Program Educational Objectives (PEOs)

PEO-I: Establish a strong foundation in computational mathematics, probability, statistics, and artificial intelligence to model, analyze, and solve complex scientific and technological problems.
PEO-II: Enable graduates to acquire practical proficiency in programming, data analysis, and intelligent system development for effective problem-solving in diverse application areas.
PEO-III: Prepare students for higher studies and professional careers in applied mathematics, artificial intelligence, and related interdisciplinary fields in academia, industry, and research organizations.
PEO-IV: Foster independent learning, critical thinking, and the ability to integrate research-based knowledge in computational and AI-driven domains.
PEO-V: Cultivate innovation, ethical awareness, and a commitment to lifelong learning to effectively respond to emerging challenges and advancements in computational and artificial intelligence technologies.

Program Learning Outcomes (PLOs)

PLO-I: Demonstrate a strong understanding of mathematical concepts, computational algorithms, and their application to solving complex, real-world problems.
PLO-II: Apply principles of artificial intelligence to design, implement, and evaluate intelligent systems using programming languages and software tools such as Python, TensorFlow, and MATLAB.
PLO-III: Develop proficiency in data-driven decision-making through statistical analysis, machine learning, and predictive modeling techniques.
PLO-IV: Employ critical thinking and research methodologies to analyze and solve interdisciplinary problems in computational mathematics and AI-related fields.
PLO-V: Integrate ethical principles and values in developing and deploying AI technologies, ensuring their responsible use across societal and industrial applications.
PLO-VI: Engage in continuous learning to stay updated with advancements in computational mathematics and artificial intelligence, fostering innovation and adaptability in a rapidly evolving technological landscape.