This talk provides a broad high-level introduction to Machine Learning and its place in the larger domain of AI. It covers the key definitions, terminology, and types of techniques used in machine-learning. It provides examples of applications of these techniques. It also covers methods and metrics to evaluate and interpret the results.
The talk is intended for beginner to intermediate audiences.
Key take-aways: 1. Definition of machine-learning in the context of AI in general. 2. Ability to distinguish between different techniques and tools used in ML at high-level. 3. Understanding of types of problems that ML can solve and how to apply ML in their work.
Experienced Data Scientist with subject matter expertise Semantic Web / Ontologies, NLP, Text Mining and Applied Machine Learning. Skilled in Enterprise Knowledge Representation and Management, EIM, Ontological / Semantic Reasoning, Text Mining and Data Modeling. Strong data science professional with industry and academic background and a Ph.D. in Computer Science from The Ohio State University.