A common headline topic these days, many are interested in the study of Artificial Intelligence and the field’s fascinating future. From personalized shopping recommendations on popular eCommerce sites to smartphone digital assistants, artificial intelligence has changed how we interact and perform tasks in our personal and professional lives.
Let’s set a definition before we dive in further. Artificial intelligence (AI) uses intelligent machines and systems to perform tasks that, in the past, were completed through human intelligence, like playing games or understanding natural language. While we cannot predict the future, it’s necessary to prepare ourselves to understand and utilize artificial intelligence to stay informed of technological advances and sharpen vital skills when entering the workforce.
Below, you’ll find an overview of what to look for and expect to learn in the AI courses offered in Master of Science in Artificial Intelligence (MS in AI) programs, including core MS AI courses, practical skills, and specialized topics that make up the MS in AI curriculum.
What is a Master’s in Artificial Intelligence?
A Master’s in Artificial Intelligence (AI) trains specialists who create intelligent machines and systems that perform tasks that would, in the past, have required human intelligence. As such, MS AI classes emphasize AI’s theoretical and practical applications with introductory and advanced MS AI-specific courses and topics.
Graduates then transfer the knowledge, skills, and training gained in their Master’s in AI classes to pursue careers in technology innovation, robotics, and autonomous vehicles. Graduates also find work with companies creating drones, self-driving cars, or customer behavior prediction.
Due to the hands-on nature of robotics, Master’s in AI classes and degree programs are typically offered in person instead of online. Additionally, a Master’s degree in Artificial Intelligence usually includes 30 to 50 credits and a capstone course or thesis specific to the student’s chosen concentration.
What MS AI classes do students take?
Enrolling and earning a Master’s in AI degree involves creative thinking and coding to construct advanced AI systems. Through face-to-face instruction, hands-on learning, and web-assisted courses, students will learn coding in programming languages like Python and R, work with AI frameworks, and develop their own AI models.
Students pursuing an MS in AI delve into these theoretical and practical applications with the following MS AI classes. Master’s in AI courses’ descriptions will vary depending on the college or university. The AI course descriptions below are from University of Bridgeport’s online course catalog system.
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Computer Vision
This project-oriented course is designed to explain computer image display, processing, and various limitations.
The processing students learn includes:
- Edge detection
- Hough transform
- Moment invariant methods
- Relaxation algorithms
- Thinning algorithms
Data science, including Big Data Systems & Analysis
This course introduces state-of-the-art computing platforms, focusing on utilizing them in processing (managing and analyzing) massive datasets.
Students will discuss the MapReduce (Hadoop) framework, which provides the most accessible and practical means of cloud computing, and the emerging distributed database and services, such as HBase and Pig/Hive, for large-scale data analysis.
Be prepared to learn several vital data processing techniques, such as simple statistics, data aggregation, join processing, frequent pattern mining, and data clustering, as well as information retrieval.
Machine Learning, such as Introduction to Autonomous Vehicles
Introduction to Autonomous Vehicles is an MS AI class that will focus on all research areas contributing to the autonomous vehicles field, including but not limited to machine learning, deep learning, computer vision, sensor fusion, and embedded systems.
Students will be involved in class lectures, readings, presentations, and discussions from the current literature on artificial intelligence and its applications to autonomous vehicles.
Natural language processing
Natural Language Processing (NLP) and its applications via Large Language Models (LLMs) have revolutionized AI.
This AI course starts with foundations of NLP and Transformer-based NLP architectures such as BERT and GPT. Then, it elaborates on the different recent improvements in Transformer-based designs, such as:
- Linformer
- Longformer
- Long-Short Transformer
- Perceiver.
- RoBERTa
- Transformer-XL
Neural networks, including Deep Learning and Advanced Deep Learning
Deep learning delves into advanced machine learning methods that use neural networks to extract higher levels of data. Personalized shopping suggestions and self-driven cars are great examples of AI that rely on deep learning.
Master’s AI classes in deep learning cover topics such as computer vision, reinforcement learning (RL), multi-agent reinforcement learning (MARL), variational inference and representation learning via Variation Auto Encoders (VAEs), generative adversarial networks (GANs), combination of VAEs and GANs in achieving zero and few-shot learning, Adversarial machine learning, advanced architectures for Natural Language Processing including Transformer, BERT, ALBERT, RoBERTa, XLNET, REALM, and more.
High-quality skills gained in Master’s in AI courses
As you can see from the course descriptions, graduates of Master’s in AI degree programs obtain various advanced, high-quality skills that make them more marketable and stand out to future employers.
These programs also allow students to pick at least one area of specialization and make their degree align more closely with their interests, needs, and goals.
University of Bridgeport, for example, offers students the choice of specializing in one or more of the following areas: Robotics and Automation, Deep Learning and Computer Vision, Data Sciences and Data Analytics, and Cybersecurity.
Hands-on experience in MS AI courses
Hands-on experience is vital in MS AI courses and degree programs overall.
As such, students need to look for AI courses that have the equipment and facilities required to adequately prepare them for the rigors of their future careers.
At University of Bridgeport, our students have access to The Interdisciplinary Robotics, Intelligent Sensing, and Control (RISC) Laboratory, a state-of-the-art 3D manufacturing facility for robotic manipulators, autonomous robots, sensory interpreters, uncrewed aerial vehicles, and drones.
Students are also encouraged to seek project and internship opportunities to provide practical experience during school and after graduation. Join communities and subscribe to online publications that will keep you in the know. Visit events, lectures, and expos as often as possible and seek continuing education opportunities.