A preparatory course that covers the fundamental constructs of the Python programming language and introduces some useful applications. The course assumes no background or experience with programming and aims to train students to write python code, which is necessary for them to develop AI applications using many currently popular Data Science and Machine Learning libraries.

This one-credit course is designed to train students and give them the mathematical background needed in problems emerging from Data Science and Machine Learning. It aims to fill in the gap between high school mathematics and the required mathematical pre-requisites. Students will be introduced to linear algebra and calculus topics which are fundamental to the methods used in the main ML and DS courses.

This course is designed to build a working background for those who want to follow a data science track. The course begins with the definition, interpretation and properties of probability, calculation of probability by methods of enumeration, conditional probability and independent events; univariate and bivariate distributions corresponding to both discrete and continuous random variables; covariance and correlation between random variables, independent random samples and the central limit theorem; basics of point estimation, interval estimation and hypothesis testing.

This course covers the mathematical underpinnings of machine learning and the practical know-how needed to effectively train, test, and deploy machine-learning models to real-world problems.

This introductory course explores the output expected of data scientists and equips students with the ability to learn from data to gain predictions and insights. Through real-world examples of wide interest, several facets of the data science pipeline and lifecycle using both the R and Python programming languages will be introduced.

This course provides an overview of deep learning methods and their related applications. It focuses on applied deep learning and includes lab assignments, practical use cases, as well as a project that explores the applications in deep learning.

This course critically examines the various ethical issues related to AI such as safety and security, privacy, transparency, accountability, bias and fairness, and reviews the technical methods to identify and address these issues.

This course focuses on Arabic natural language processing (NLP) and covers its foundational concepts such as tokenization, part-of-speech tagging, syntactic parsing, word sense disambiguation, and semantic representations. It also uncovers NLP’s applications including information retrieval, machine translation, sentiment and emotion analysis, dialogue systems, and question answering.

This course provides students with an introduction to the diverse applications of artificial intelligence (AI) in healthcare research while addressing its limitations and ethical implications. Students will learn about different data representations and sources, and how machine-learning techniques can be used to address various health problems. The course also touches upon AI interpretability and important ethical considerations.

This course explores the foundations, architectures, and applications of Large Language Models (LLMs), focusing on Transformers, self-supervised learning, and fine-tuning techniques. Students will gain hands-on experience developing generative AI applications, including prompt engineering, retrieval-augmented generation, and deployment strategies. Ethical considerations, and real-world case studies will be integrated to ensure a practical understanding of LLMs

It is now a critical strategic advantage for companies to use data effectively to drive rapid, precise and profitable decisions. More specifically, customers today expect to receive outcomes delivered to them based on their preferences; hence, companies must leverage the available wealth of customer data and take customers farther along their journey.
This course explores the growing important role of data in business and covers the key concepts of customer analytics with quantitative strategies to answer different business questions. The aim is to demystify the role of data and AI in impacting customer behavior. Students will learn about AI-powered applications that can enhance the customer journey. The course utilizes relevant theory, empirical analysis, and practical examples to develop the key learning points. By the end of this course, students will have the ability to envision how data, AI and Machine Learning can be used to enhance the business.

This course introduces the principles and architectures behind agentic AI systems, focusing on the design and implementation of LLM-based agents that can reason, plan, and act. Students learn to integrate external tools and knowledge sources, build agents with memory and retrieval-augmented generation capabilities, and develop multi-agent workflows. The course also covers agent evaluation, reliability, and safety considerations, culminating in a capstone project where students build and present a practical agentic application.