Artificial Intelligence (AIG)

This course introduces students to the fundamental concepts of machine learning such as supervised, unsupervised, regression analysis, classification, clustering, dimensionality reduction, and reinforcement learning as it applies to different application domains. The course will cover a range of machine learning algorithms such as decision trees, random forests, support vector machines, neural networks, and deep learning models. The course will also focus on the practical aspects of machine learning, including data preprocessing, feature selection, model evaluation, and interpretation of results using contemporary machine learning libraries.

Students will learn what Artificial Intelligence (AI) is and investigate use cases and applications of AI. They develop the skills to differentiate between artificial intelligence (AI) and non- AI implementations, and to decide whether an AI-based approach is feasible, or beneficial. AI concepts and terms like machine learning, deep learning and neural networks are explored in this course.

This course provides students with an introduction to decision theory and the effective use of evidence-based decision making to inform strategic decisions. Students examine the personal and social predispositions that inform decision making, and organizational conditions in order to understand the complexity of decision-making processes. In the course, students are introduced to the different methods and methodologies for external, competitor, and opportunity analyzes to support the development of a business strategy.

This course provides the students with a comprehensive understanding of Cloud Computing for Machine Learning. Students will gain knowledge and hands-on skills to utilize cloud services such Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure to design and implement a machine learning pipeline. This includes data preparation, model training and evaluation, and model deployment.

This course introduces advanced tools and techniques for analyzing, visualizing, and communicating data. Students will explore the fundamental principles and strategies to produce graphs that support the analysis of data and demonstrate patterns, trends, errors and discrepancies. Students will also learn how to combine high velocity data streams for dashboard design. Students will learn and practice document coding, writing reports, conducting data exploration and rationalizing its usage, explaining network architecture, discussing results and defining metrics.

This course will cover the basics of data preparation, including cleaning, transformation, and integration techniques, but with an emphasis on how these techniques differ for use in AI models. The course will also cover the different types of data commonly used in AI, such as structured, unstructured, and semi-structured data, and how to prepare each type for use in AI applications. In addition, this course will cover the governance of data in AI, including privacy laws, ethical considerations, and responsible AI practices. Students will learn about the importance of transparency, accountability, and explainability in AI models and how to ensure these qualities in the data preparation and governance process.

The purpose of this course for students to apply the knowledge acquired during the program to a project in a realistic setting. Students will design a capstone project in the field of artificial intelligence. During the project, students engage in the entire process of articulating business problems and client needs and solving a real-world AI problem from designing an AI strategy, to implementing the solution and presentation.

This course introduces the fundamentals of image processing and explores the intersection of computer vision and deep learning. Students will explore deep learning methods for computer vision applications. They will learn to build, train, test, and evaluate deep neural networks for computer vision applications. Students will learn to use TensorFlow and Pytorch for computer vision applications as common deep learning frameworks used in the industry.

This course introduces the fundamentals of image processing and explores the intersection of computer vision and deep learning. Students will explore deep learning methods for computer vision applications. They will learn to build, train, test, and evaluate deep neural networks for computer vision applications. Students will learn to use TensorFlow and Pytorch for computer vision applications as common deep learning frameworks used in the industry.

This course focuses on how to work with text data while applying traditional and more advanced NLP techniques to solve practical problems. Students will learn traditional and more modern NLP techniques and various NLP algorithms and applications and work with the learned algorithms and pre-existing static datasets to solve issues. Students will apply this knowledge by evaluating several implemented NLP systems and be required to provide justifications for and against each system.

This course provides a technical foundation into design and development of embedded software for robotics control, navigation, and perception. Students will be introduced to the autonomous robotics terminologies and exposed to machine learning applications in robotics.