About this course
Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment. They are made possible by advances in a number of scientific fields over the past half-century, and are different in important ways from the information systems that preceded them.
Those systems have been deterministic; cognitive systems are probabilistic. They generate not just answers to numerical problems, but hypotheses, reasoned arguments and recommendations about more complex — and meaningful — bodies of data.
What’s more, cognitive systems can make sense of the 80 percent of the world’s data that computer scientists call “unstructured.” This enables them to keep pace with the volume, complexity and unpredictability of information and systems in the modern world. The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives save.
This course covers background concepts needed to understand and work with cognitive technologies.
Course Outline
§ Introduction to Cognitive Computing
§ The Deep QA Architecture
§ Semantic Integration and Machine Learning
§ Natural Language Processing in Watson
§ Unstructured Information Management Architecture
§ Structured Knowledge in Watson
§ Domain Adaptation
§ Distributional Semantics
§ Labs
– UIMA hands on
– Watson ecosystem
– Watson Path and WEA
§ Project
Audiences and prerequisites
For people who possess Machine Learning, Natural Language processing or text
mining, computer science, statistics, etc. background.