Machine learning and the algorithms that fuel its applications have important principle foundations including deep learning neural networks, increased complexity in evolving neural networks, and robotics developments which are increasingly intelligent, adaptable, and resilient. Also known as reinforcement learning, algorithms can learn from experience to make decisions or provide diagnostics in applications such as educational software, healthcare decision making, robotics, or people-facing applications. This session explains the basic elements of machine learning, and the typical environment for building and testing neural networks and reinforcement learning.
