Artificial intelligence

Core Courses

A)    Common Core Curriculum: First Four Semesters

Analysis 1 ,2 & 3

Algebra 1 , 2 & 3

Algorithms and Data Structures 1,2 & 3

Machine Structure

Graph Theory

Information Systems

Computer Architecture (CA)

Probability and Statistics

Introduction to Databases

Introduction to Networks

Theory of Languages

Operating Systems 1 & 2

Mathematical logic

Fondamentals of Electronics

Object-Oriented Programming 1 & 2 (OOP)

B)    Specialization Track (6 Semesters)

1. Systems & Infrastructure

·        Database Systems: Design and Administration (SQL/NoSQL, query optimization)

·        Compiler Design (Lexical analysis, parsing, code generation)

·        Distributed Computing Techniques (Cloud architectures, consensus protocols)

·        Blockchain (Smart contracts, decentralized systems)

2. Artificial Intelligence

·        AI Foundations (Search algorithms, knowledge representation)

·        Machine Learning (Supervised/unsupervised learning)

·        Deep Learning (CNNs, RNNs, transformers)

·        Generative AI (GANs, diffusion models, LLMs)

·        Natural Language Processing (NLP) (Text mining, sentiment analysis)

·        Computer Vision (Object detection, image segmentation)

3. Data Science & Analytics

·        Big Data Processing (Hadoop/Spark, stream processing)

·        Business Intelligence (ETL, OLAP, dashboards)

·        Data Visualization (Tableau, D3.js, storytelling)

·        Knowledge Representation and Reasoning (Ontologies, semantic networks)

4. Optimization & Modeling

·        Linear and Dynamic Programming (Simplex method, Bellman equations)

·        Operational Research & Optimization (Supply chain, logistics)

·        Nature-Inspired Optimization Algorithms (Genetic algorithms, swarm intelligence)

·        Modeling and Simulation (Discrete-event, Monte Carlo)

5. Software & Development

·        Software Engineering (Agile, CI/CD, design patterns)

·        Web Development (Frontend/backend, REST APIs)

·        Computational Methods I, II (Numerical analysis, PDE solvers)

Advanced Topics

 Hyperparameter Optimization & AutoML

 Metaheuristics for CNN Architecture Search

-         Genetic algorithms (NSGA-II) for layer optimization

-         Particle swarm optimization (PSO) for kernel tuning

-         Multi-objective optimization (accuracy vs. latency)

Automatic CNN Tuning Frameworks

-         Neural Architecture Search (NAS) with Bayesian optimization

-         Automated pruning/quantization using reinforcement learning

2. Advanced Speech Processing

Arabic Speech Recognition

-         ArabicBERT Transformer: Fine-tuning for dialectal ASR

-         Wav2Vec 2.0 Adaptation:

Low-resource training techniques

 Cross-lingual transfer learning

3. Vision Transformer (ViT) Systems

 ViT for Medical/Remote Sensing

-         Attention mechanisms for high-resolution images

-         Hybrid CNN-ViT architectures

-         Explainability via attention visualization