Courses
Applied Artificial Intelligence Courses
AAI202 Python for AI and Data Science
[3–0, 3 cr.]
This course provides a comprehensive introduction to Python programming for data science and AI applications. Students will learn the fundamentals of Python, along with techniques for data analysis, exploration, and visualization. Key topics include data manipulation with Pandas and NumPy, statistical and visual exploration with Matplotlib and Seaborn, and introductory machine learning workflows using Scikit-learn.
AAI211 AI Ethics and Responsible Data Use
[1–0, 1 cr.]
This course explores the ethical, legal, and societal implications of artificial intelligence and data-driven technologies. Students will examine key topics such as privacy, fairness, bias, accountability, and the ethical deployment of AI systems. The course covers responsible practices for collecting, storing, and using data, strategies to identify and mitigate bias in datasets and AI models, and approaches to ensure transparency and accountability in automated decision-making. Through case studies, discussions, and practical exercises, the course equips students with the skills to critically evaluate AI projects and develop responsible, ethical solutions in real-world contexts.
AAI461 Introduction to Machine Learning
[3–0, 3 cr.]
This course provides an overview of theoretical and application aspects of machine learning. Topics include supervised and unsupervised learning including generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, clustering, dimensionality reduction, and kernel methods. The course also covers learning theory, reinforcement learning, adaptive control. An applied approach will be used, where students get hands-on exposure to ML techniques through the use of state-of-the-art machine learning software frameworks.
AAI462 Fundamentals of Deep Learning
[3–0, 3 cr.]
This course presents an introduction to deep learning and its applications. Topics include introduction to neural networks, regularization, convolution neural networks, auto-encoders, recurrent neural networks, long short-term memory networks, generative adversarial networks, and reinforcement learning. An applied approach will be used, where students get hands-on exposure to AI techniques through the use of state-of-the-art machine learning software frameworks.
AAI464 Deep Learning for Natural Language Processing
[3–0, 3 cr.]
Understanding complex language has wide applications in web search, advertisement, customer service, automatic translation, chat bot engineering, etc. Many different machine learning techniques are at the heart of natural language processing (NLP) applications. Recently, Deep Learning (DL) approaches have obtained very high performance across many different NLP tasks. This course covers such approaches. Students will build their own neural network model and apply it to a large scale NLP problem. From the model side, the following topics will be covered: word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks. From the NLP side, the course covers the following topics: syntax parsing, vector space modeling, dimensionality reduction, speech tagging, text classification, and sentiment analysis.
AAI601 Mathematics for Applied AI
[3–0, 3 cr.]
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.AAI602 Programming for Applied AI
[3–0, 3 cr.]
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.AAI611 Machine Learning Fundamentals and Applications
[3–0, 3 cr.]
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.AAI612 Deep Learning and its Applications
[3–0, 3 cr.]
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. The following topics will be covered with a hands-on approach: feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs). The course will include hands-on applications covering natural language processing, behavioral analysis, and anomalies detection.AAI613 Computer Vision and its Applications
[2–0, 2 cr.]
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 and tools used on these applications.AAI614 Data Science and its Applications
[3–0, 3 cr.]
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.AAI615 Ethics and AI
[1–0, 1 cr.]
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 desicions 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.AAI631 Data Visualization
[2–0, 2 cr.]
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.AAI632 Big Data Analytics
[2–0, 2 cr.]
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.AAI633 Introduction to Generative AI
[2–0, 2 cr.]
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.
AAI634 Data Engineering
[1–0, 1 cr.]
AAI635 Recommender Systems
[1–0, 1 cr.]
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.
AAI641 Healthcare Analytics
[3–0, 3 cr.]
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.