This course provides a rigorous introduction to the fundamental principles and techniques of Artificial Intelligence (AI), with an emphasis on intelligent decision-making, autonomous behaviour, and data-driven reasoning relevant to modern naval and defence applications. Designed for undergraduate naval officer training, the course develops both theoretical understanding and practical insight into how AI systems perceive environments, reason under uncertainty, learn from data, and support mission-critical decisions.
The course covers core AI paradigms, including problem formulation, state-space search, heuristic optimization, knowledge representation, and reasoning. It introduces machine learning concepts such as supervised and unsupervised learning, probabilistic models, and basic neural networks, enabling students to understand how intelligent systems adapt and improve performance over time. Special attention is given to AI techniques used in autonomous and semi-autonomous systems, including planning, perception, and real-time decision support.
Students will also explore applications of AI in areas relevant to naval operations, including autonomous vehicles, surveillance and reconnaissance, predictive analytics, and intelligent command-and-control systems. Ethical considerations, safety, robustness, and trustworthiness of AI systems in defense environments are discussed to develop responsible and informed future officers.
By the end of the course, students will be able to analyze AI-based solutions, understand their operational capabilities and limitations, and apply foundational AI concepts to solve practical problems in complex and dynamic environments.
This course provides an in-depth and rigorous treatment of deep learning, focusing on the theoretical foundations, algorithmic principles, and practical methodologies that underpin modern deep neural networks. Deep learning has transformed fields such as computer vision, speech processing, and natural language understanding and is increasingly influencing a wide range of scientific and engineering disciplines. The course is designed for postgraduate students seeking both strong conceptual understanding and research readiness in deep learning.
The course emphasizes supervised deep learning, covering feedforward neural networks, convolutional neural networks, recurrent and sequence models, attention mechanisms, and transformer architectures. Students will study optimization techniques, regularization strategies, generalization theory, loss functions, and training dynamics of deep models. Mathematical perspectives on representation learning, expressivity, and convergence behavior are discussed to provide theoretical insight into why deep networks work.
In addition to supervised learning, the course provides substantial coverage of unsupervised and self-supervised learning methods, including autoencoders, variational autoencoders, contrastive learning, and representation learning without labeled data. Topics such as transfer learning, domain adaptation, and foundation models are introduced to connect classical deep learning with modern research trends.
Advanced topics may include deep generative models, multimodal learning, physics-informed deep learning, and large-scale neural architectures, depending on student background and research interests. Through programming assignments, critical paper reviews, and a research-oriented project, students will gain hands-on experience in designing, training, and evaluating deep learning models for real-world and research problems.
By the end of the course, students will be able to critically analyze deep learning research, implement state-of-the-art models, and apply deep learning techniques to complex, high-dimensional datasets in both academic and industrial research settings.
This course provides a comprehensive and rigorous introduction to Natural Language Processing (NLP), the field concerned with enabling computers to process, understand, and generate human languages. The course explores a wide range of computational methods for representing linguistic structure and meaning, and for designing intelligent systems that operate on text and speech data. Applications include machine translation, text summarization, sentiment analysis, information extraction, question answering, and conversational agents.
NLP is inherently multidisciplinary, drawing on ideas from machine learning, linguistics, probability theory, and artificial intelligence. Accordingly, this course integrates linguistic concepts with data-driven and learning-based approaches. Students will study computational representations of language at multiple levels, including words and subwords, phonetics and phonology, morphology, syntax, semantics, discourse, and dialogue. The interaction between these levels is emphasized to reflect how state-of-the-art NLP systems achieve robust performance on complex tasks.The course covers both classical and modern NLP techniques, including n-gram language models, probabilistic sequence models, word embeddings, neural sequence models, attention mechanisms, and transformer-based architectures. Students will learn how large-scale text data and probabilistic modeling enable machines to capture linguistic regularities and semantic relationships. Special emphasis is placed on deep learning approaches that underpin contemporary NLP systems.
Through hands-on assignments, critical reading of research literature, and a research-oriented project, students will gain practical experience in building, evaluating, and analyzing NLP systems. By the end of the course, students will be able to understand and implement modern NLP algorithms, critically assess research contributions, and apply NLP techniques to real-world and research problems involving text and speech.
Natural Language Processing (Postgraduate) PNEC NUST
Deep Learning (Postgraduate) PNEC NUST
Artificial Intelligence (UTOs) PNEC NUST
Machine Learning (Postgraduate) Abasyn University
Computational Techniques (Postgraduate) Abasyn University
Pattern recognition (Postgraduate) Abasyn University
Advanced Power Electronics (Postgraduate) PNEC NUST
Selected Topics in Power Systems (Postgraduate) PNEC NUST
Research Methodology (Postgraduate) Abasyn University
Power system stability and control (Undergraduate) CoEME NUST
Power System Analysis and Design (Undergraduate) CoEME NUST
Power Electronics (Undergraduate) CoEME NUST
Electronics devices and circuits (Undergraduate) CoEME NUST
Electrical Engineering (Undergraduate) CoEME NUST
Power Distribution and Utilization (Undergraduate) PNEC NUST