Online Master’s in Applied Artificial Intelligence: Curriculum
30 total credits required
The online Master’s in Applied Artificial Intelligence degree requires the completion of 11 total courses, including eight core courses and three electives. Choose from elective options in Business and e-Commerce, Healthcare, or Digital Humanities.
The program can be completed entirely online in just two years. Each course is taught by industry-experienced faculty to ensure you graduate fully prepared to put your new AI tech skills to work.
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 balance 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.
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), keypoint and edge detection, image segmentation, perceptual grouping, object/activity recognition, pose estimation, and 3D scene reconstruction. Students will learn about advanced AI techniques 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. Particular 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. Readings will be drawn from a host of books including: Heartificial Intelligence: Embracing Our Humanity to Maximize Machines by John Havens; Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin; Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor by Virginia Eubanks; Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard; and Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez.
This course focuses on the application of the various learned AI techniques and algorithms for solving real life industrial, social, business or medical problems. Students will apply the AI tools, knowledge and skills to solve an actual real life problem of their interest or related to their work field. This course requires an independent development and documentation of the proposed solution.
This course covers essential and practical skills necessary to clearly and effectively communicate information from data through graphical means, based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course introduces the value of data visualization, as well as the principles and techniques of scientific visualization. It also focuses on big data organization and mining for decision support, and on how to best leverage visualization methods. Assignments and projects will involve the use of different tools and resources to manipulate and visualize data with code.
This course provides an understanding of the business value of big data, the importance of effective management of big data, and the development of technical competencies using leading-edge platforms for managing and manipulating structured and unstructured big data.
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 economics and regulation for AI, electronic health records, deep learning in genomics, pharmacology, and multi-scale omics data, everaging big data for personalized treatment and treatment response prediction (precision medicine), and reeinforcement learning for medical decision making.
- Healthcare economics and regulation for AI
- Electronic health records
- Deep learning in genomics, pharmacology, and multi-scale omics data
- Leveraging big data for personalized treatment and treatment response prediction (precision medicine)
- Reinforcement learning for medical decision making
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.
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.
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