MASTER OF SCIENCE IN APPLIED ARTIFICIAL INTELLIGENCE

Interest in and usage of Machine Learning systems has increased dramatically in recent years. More and more innovative products and research rely on Machine Learning systems that leverage data to make predictions and identify trends. However – as with many cutting-edge fields – Machine Learning systems are often implemented improperly. As a result, many Machine Learning systems are unreliable, inefficient, or even useless. Machine Learning Operations (MLOps) is a methodology whose goal is to design, build, deploy, and maintain machine learning models properly. MLOps combines practices from Machine Learning, Data Engineering, and DevOps to ensure that Machine Learning models and algorithms are reliable, efficient, and – most importantly – useful. This course will introduce students to the key concepts of MLOps and a holistic method of designing suitable ML systems. Students will learn and perform the best practices for building Machine Learning systems with hands-on learning experiences and real-world applications. While students will learn about and implement some Machine Learning algorithms in this course, this course is not intended to teach them about the field of Machine Learning. Rather, students will learn how to properly design Machine Learning systems throughout the entire lifecycle.

Prerequisites: AAI 510, AAI 511, AAI 520, AAI 521, AAI 530, and AAI 531

Artificial Intelligence (AI) and Machine Learning (ML) are transforming society through their ability to model complex systems and compute how to act effectively and safely in a wide variety of situations. This course provides a structured introduction to the fundamental principles and core techniques that underpin AI/ML, while also exploring their practical applications and limitations. Students will engage with topics ranging from intelligent agents and heuristic search to mathematical optimization, supervised and unsupervised learning, forecasting, deep learning architectures, and reinforcement learning. The course emphasizes critical thinking about problem formulation, algorithm selection, and evaluation, preparing students to navigate the full lifecycle of AI/ML projects. Real-world applications in areas such as computer vision, natural language processing, forecasting, and decision-making are examined to connect theory with practice. By the end of the course, students will have the knowledge necessary to apply these techniques in a wide range of professional contexts.

This course provides a comprehensive introduction to AI Agents, covering fundamental concepts, popular frameworks, essential tooling, and practical development. Students will learn to build, evaluate, and manage AI agents, culminating in an integration of subject matter expertise into agent outputs and the ethical considerations surrounding this rapidly evolving field.

Prerequisites: AAI 500 and AAI 501

Neural networks have enjoyed several waves of popularity over the past half-century. The many applications of neural networks include apps that identify people in photos, automated vision systems for large-scale object recognition, smart home appliances that recognize continuous, natural speech, self-driving cars, and software that translates from any language to any other language. In this course, students will learn the fundamental principles and concepts of neural networks and state-of-the-art approaches to deep learning using in-demand Python packages, such as TensorFlow and PyTorch. Students will learn to design neural network architectures and training methods using hands-on assignments and will perform comprehensive final projects in this course. Prerequisites: AAI 500 and AAI 501