Online Master’s in Applied Artificial Intelligence: Curriculum

(*May be substituted with an elective if the student has Computer Science or Computer Engineering major with the required math background)

This course covers the mathematical principals required for the various concepts in the area of applied artificial intelligence. This course aims at delivering the mathematical topics in a balanced manner based on solid theoretical foundation while focusing on the computational aspects and application to data problem. Topics covered include linear algebra, multivariate calculus, optimization, regression, statistics of datasets, orthogonal projections, principal component analysis, and probability. The course provides computational and practical examples of the covered topics.

(**May be removed as prerequisite requirement if the student has the python programming and required libraries background.)

This course covers programming techniques used in AI applications. Topics include programming constructs, I/O, conditional constructs, iterative control structures, structured decomposition, method call and parameter passing, classes, 1-D and 2-D arrays, libraries, APIs, and Data Structures. The course will use Python with several tools where students learn programming with a beginner-friendly introduction to Python and AI libraries including learning how to analyze data, integrate and use basic machine learning algorithms and APIs, create visualizations, implement and test some models, and analyze results.

This course covers the essential machine learning techniques and algorithms and their applications. Topics include supervised and unsupervised learning, clustering, classification algorithms, linear regression, support vector machines, decision trees, random forests, neural network, deep learning, and reinforcement learning. Throughout the course, students will be exposed to real-world industrial, business, medical and social problems, where the obtained skills are employed to handle data and develop machine learning based solutions. The material and structure of the course are designed with a preference for the practical knowledge of AI more than mathematical or theoretical concepts. Different Machine Learning applications will be discussed including computer vision, natural language processing, time-series prediction, speech recognition, sentiment analysis, cybersecurity, among others.

This course covers principles of deep learning and in its applications. Students will learn how to build and use different kinds of deep neural networks using hands-on approach. Topics include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers and encoders/decoders. The course will include hands-on applications covering natural language processing tasks, behavioral analysis, financial analysis and anomalies detection.

The course covers Artificial Intelligence and Machine Learning methods for computer vision. Fundamental concepts in computer vision are covered, including image formation, feature representation (color, texture, and shape), image augmentation (filtering), key point and edge detection, image segmentation, perceptual grouping, object/activity recognition, pose estimation, and 3D scene reconstruction. Students will learn about advanced AI techniques and tools used on these applications.

Data science enables us to process big amounts of structured and unstructured data to detect patterns and perform in-depth and conclusive analysis. This course covers the main techniques involved in the data processing pipeline, including data capture (scraping, cleaning, and filtering), feature engineering (representation, selection, and transformation), data augmentation (knowledge-based and corpus-based), data mining (regression analysis and predictive modeling), and data visualization (search and exploration). Real-life applications will be considered including search engines, text summarization, text auto-correction, chat bots, personal assistants, social network analysis, sentiment analysis, and event detection, among others. Students will learn about advanced AI techniques and tools used on these applications.

It is often said that an ethical AI system must be inclusive, explainable, have a positive purpose and use data responsibly, but what does this mean in practice? In this course, students will examine and discuss case studies showcasing both good and questionable applications of AI. Emphasis will be placed on the data and modeling decisions that AI developers can make in the creation and application of their systems and the implications of these decision on society.

This course entails an independent development, and documentation of substantial AI project using techniques and/or tools. The course includes periodic reporting of progress, plus a final oral presentation and written report.

May be substituted with AAI699O upon the approval of the program director.

This course entails the application of research methods to a current topic relevant to Applied AI. The thesis must incorporate the student’s hypothesis, test methods, test results, and conclusions ready for further publications.

This course covers the essential and practical skills necessary to communicate information about data clearly and effectively through graphical means based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course introduces the value of visualization, the principles and techniques of data visualization, and specific techniques in information and scientific visualization. The course also focuses on big data organization and mining for decision support, and how to best leverage visualization methods. The assignments will involve the use of Tableau, Seaborn, Dash, Folium, Matplotlib, and the ability to manipulate data sets with code.

The course covers data management and systems aspects of big data. Topics include an overview of big data management systems, distributed big-data storage, Programming models in big data, column-based storage, analytics on big data, big spatial data, and document databases.

This course provides a theoretical foundation and practical skills for Generative AI. Topics include anatomy of generative models, transformers, GAN, the Diffusion Model for images. Students will be equipped with the skills to leverage state of the art techniques for visual representation, generative text, text to image synthesis and more.

This course introduces the ETL pipeline: Extract, Transform, and Load. The course provides students with a technical overview on how to source, prepare, and manage data. Students also will be introduced to Dash and to the principles of NoSQL database systems.

The course covers the concepts, applications, algorithms, programming, and design of recommender systems. Topics include techniques for making non-personalized, content-based, and collaborative recommendations and their evaluation, evaluation methods, contextual bandits, ranking methods, and fairness and discrimination.

This is an introductory course to the healthcare research fundamentals and methodologies. Topics include healthcare research design, data collection, data analysis, and operations research and operations management tools applied to the health care management sector.

This course covers the essential and practical skills for applying AI in biomedical informatics. Topics include healthcare research design, data collection and integration, data analysis with a focus on machine learning in genomics, pharmacology, multi-scale omics data analysis, as well as personalized treatment and precision medicine.

This course covers the essential and practical skills for applying AI in medical diagnosis and prediction. Topics include: medical image classification, detection, segmentation, and reconstruction, time-series classification, regression, and forecasting, Weakly-, Semi-, and Self-supervised learning, and fairness and robustness.

• Medical image classification
• Medical image detection and segmentation
• Time-series classification
• Time-series forecasting
• Weakly-, Semi-, and Self-supervised learning
• Fairness and robustness

This course covers word vector representations, embeddings, syntax parsing, vector space modeling, dimensionality reduction, speech tagging, text classification, sentiment analysis, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks.

Throughout this journey, students will engage with concepts of culture and society through the use of un(der)explored datasets of text, image and sound, while embracing new computational frameworks that are increasingly powerful and easy to use. We focus on the importance of an interdisciplinary, liberal arts mindset in the domain of AI. This course covers the essential and practical skills for applying AI in digital humanities. Topics shall cover AI models, techniques and tools for digital content and resources including text and web mining and analysis, automatic language analysis, sentiment analysis graphs and reports, opinion mining, topic modeling, identification of ideas, web structure mining, web usage mining, language models, visualizations of large image sets, 3D modeling and classification of historical artifacts, and AI driven creation of music, images, and literature.

This course leverages the fundamental design thinking process as a framework by which to embed AI tools to innovatively solve problems and address business challenges. Specifically, students will use machine learning tools to identify user groups within the early phases of the empathize stage; AI driven NLP tools to automate detection of key themes in interview transcripts and social media posts as they write problem statements in the define stage; design thinking tools to brainstorm solutions in the ideate stage; optimization tools to ascertain the best service layouts or user interfaces in the prototype stage; and usage analytics tools as they provide data to understand how the user interacts with the final solution.

Selected advanced topics in Applied AI. This course shall covers cutting-edge topics in the field of Applied AI. Topics include cyber security, internet of things, internet of vehicles, metavers, virtual reality, computational social sciences, among others. This course can be taken several times under different topics.