Certificate in Fundamentals in AI for Business
How companies boost productivity, enhance products and services, and drive innovation looks fundamentally different today than it did just a few years ago – and artificial intelligence is why. As organizations race to transform how they operate, they need leaders who understand what AI actually is and how to deploy it effectively with sound ethics and critical judgment.
Stevens has long led the way in how data and analytics are taught in a business context. Now we're doing the same for AI, integrating it across our curricula and programs. The Fundamentals in AI for Business Certificate gives you a strong foundation in practical, hands-on skills you can apply to the real scenarios you'll face at work.
You can tailor your coursework to your interests. Whichever courses you choose, you'll build a well-rounded skill set spanning foundational theory, technical algorithms and engineering applications across artificial intelligence, machine learning and deep learning before gaining experience applying these concepts throught a business and technology lens.
This certificate is ideal for professionals looking to up-skill, as well as current undergraduate or graduate students looking to strengthen the value of their Stevens degree through a seamless integration into your current coursework.
Program Structure
Select one course from each row.
Core Course 1* | AAI 551 - Engineering Programming: Python | CS 515 - Fundamentals of Computing OR CS 541 - Artificial Intelligence |
Core Course 2 | AAI 595 - Applied Machine Learning | CS 559 - Machine Learning: Fundamentals and Applications |
Department Course | BIA 568 – Management of AI Technologies |
*If the student has sufficient programming background/experience, CS 508 - Human-Centered AI can be used for core course 1
Curriculum
AAI 551 Engineering Programming: Python
This course presents tool, techniques, algorithms, and programming techniques using the Python programming language for data intensive applications and decision making. The course formally introduces techniques to: (i) gather,(ii) store, and (iii) process large volumes of data to make informed decisions. Such techniques find applicability in many engineering application areas, including communications systems, embedded systems, smart grids, robotics, Internet, and enterprise networks, or any network where information flows and alters decision making.
AAI 595 Applied Machine Learning
An introduction course for machine learning theory, algorithms and applications. This course aims to provide students with the knowledge in understanding key elements of how to design algorithms/systems that automatically learn, improve and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. The students will have chances to simulate their algorithms in a programming language and apply them to solve real-world problems.
BIA 568 Management of AI Technologies
Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.
CS 515 Fundamentals of Computing
This is an introduction to computer science with an emphasis on programming, in Python. The topics include: design; algorithmic thinking; recursion; object-oriented programming; and some basics about computer systems: machine language, interpreters, compilers, and data representation. Undergraduates are not allowed to enroll.
CS 541 Artificial Intelligence
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. The course will emphasize on both learning and problem solving, and will develop rigorous statistical models for real-world AI applications. The course will also deliver modern optimization techniques to find an optimal model for a given problem. It will require a math background in calculus, linear algebra and probability, and programming skills in Python or Matlab.
CS 559 Machine Learning: Fundamentals and Applications
In this course we will talk about the foundational principles that drive machine learning applications and practice implementing machine learning algorithms. Specific topics include supervised learning, unsupervised learning, neural networks, and graphical models. The main goal of the course is to equip you with the tools to tackle new ML problems you might encounter in life.