This course offers a foundational overview of Artificial Intelligence (AI), introducing students to its core concepts and techniques. Students will explore AI's history, current applications, and future potential, emphasizing the principles and practices that form the basis of intelligent systems. The curriculum includes various AI methodologies, such as search algorithms, machine learning, neural networks, and natural language processing. Students will develop feedforward and backpropagation neural networks. Topics include regression, classification, and reinforcement learning. Additionally, the course will address practical applications of AI in engineering and consider the ethical implications associated with AI technologies in the field.
- Teacher: Abdullah Murtadha
This course provides a comprehensive introduction to Machine Learning (ML) with a focus on its applications in robotics. It covers fundamental concepts, mathematical foundations, and essential techniques for supervised and unsupervised learning. Students will explore regression and classification models, clustering methods, dimensionality reduction, deep learning architectures, and reinforcement learning. Additionally, topics on model evaluation, deployment, and ethical considerations in ML are included. Hands-on programming exercises using Python, Pandas, and NumPy will enable students to apply ML techniques to real-world problems, particularly in vision-based and robotics applications.
- Teacher: Abdullah Murtadha
This course introduces AI & Robotics engineering students to the theory, architecture, and applications of large language models (LLMs). It covers foundational concepts in language modeling, sequential neural architectures, and transformer-based models, culminating in real-world applications like ChatGPT, retrieval-augmented generation (RAG), and language model agents. Emphasis is placed on developing hands-on skills with modern tools such as PyTorch and Hugging Face, along with a critical understanding of ethical and societal considerations surrounding LLMs. This course bridges theoretical understanding and practical implementation, preparing students for advanced AI applications in engineering contexts
- Teacher: Abdullah Murtadha
