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.

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 aims to expand on previously acquired principles in machine learning and data science to work through applications that demonstrate social impact and data-driven decision-making in the field of public policy. Using publicly available datasets and a mix of tools covering exploratory analysis, predictive analytics, spatial analytics, and NLP, this course will walk students through real-life, practical examples that demonstrate the effectiveness of those techniques in the public realm.