Applied Artificial Intelligence (AI) from Concept to Market
This course provides an overview of the processes for specifying, designing, and launching AI/Deep Learning products. AI/Deep learning applications are predicted to bring the next wave of disruptive innovation to many industries. In addition to the highly visible applications such as Autonomous Cars, Grand Master Chess winning Apps, Industrial Robots, Customer service Chatbots, AI software is already changing work processes in many professions such as law, Journalism, finance and medicine. Many platforms have become available to assuage the growing appetite for new AI/deep learning applications. The course will give an overview of the software platforms available with detailed examination of one of the major tools. The course includes a team project where the business and technical methods presented are used to launch a virtual AI/deep learning product. Students interested in registering for this course should be familiar with the programming language, Python.
- AI/Deep Learning Product design from concept to Launch
- How to find and use the most Appropriate Software Development Tools, Frameworks and SDKs available to build an AI App
- Introduction to AI/Deep Learning Product Design, Development and Launch
- New Product Development methods appropriate for launch within a large company or a Startup
- Return on investment analysis and design methodologies to maximize competitive advantage of a new AI product
- Overview of the AI/Deep Learning field and where to look for resources.
Session 1: Intro to AI Applications in Machine Vision, Chatbots, NLP, Speech Recognition, Image Recognition, Automatous Vehicles, Recommendation Engines, Chess and other Game Playing Apps
Session 2: Introduction to Methods used in Deep Learning
Session 3: Overview of Open Source Software Development Kits SDKs including Tensorflow
Session 4: AI product Design Introduction to AI/Deep Learning Product Design, Development and Launch
Session 5: Use Case: Selection of use application such as Handwriting Recognition , Machine Vision, Speech Recognition or ChatBot
Session 6: Implement initial Tensorflow code
Session 7: Train and Test Model
Session 8: Verification
Session 9: Build Prototype
Session 10: Iterate Prototype design
Session 11: Complete Prototype
Session 12: Go to Market Plan
Session 13: Business Case
Session 14: Final Presentation of AI Product
Homework assignments and a group project
Professor Richard Mammone is a professor of Rutgers School of Engineering as well as the Rutgers Business School. He has created successful AI/Deep Learning Products for Speech Recognition, Face Recognition and Breast Cancer Diagnosis. He has published numerous peer reviewed papers and holds several patents in the field. His work in deep learning applications has been recognized by the Thomas Edison Award and fellowship in the National Academy of Inventors NAI. He has also founded several AI/deep learning startups.