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Machine Learning Introduction

Machine Learning Introduction incldues why is machine learning important? How Does Machine Learning Work? Types of Machine Learning, Supervised Learning, Forms of Supervised Learning, Bayesian Learning, Learning in Bayesian Networks. | Machine Learning Introduction • Why is machine learning important? – Early AI systems were brittle, learning can improve such a system’s capabilities – AI systems require some form of knowledge acquisition, learning can reduce this effort • KBS research clearly shows that producing a KBS is extremely time consuming – dozens of man-years per system is the norm • in some cases, there is too much knowledge for humans to enter (e.g., common sense reasoning, natural language processing) – Some problems are not well understood but can be learned (e.g., speech recognition, visual recognition) – AI systems are often placed into real-world problem solving situations • the flexibility to learn how to solve new problem instances can be invaluable – A system can improve its problem solving accuracy (and possibly efficiency) by learning how to do something better How Does Machine Learning Work? • Learning in general breaks down into one of two forms – Learning something new • no prior knowledge of the domain/concept so no previous representation of that knowledge • in ML, this requires adding new information to the knowledge base – Learning something new about something you already knew • add to the knowledge base or refine the knowledge base • modification of the previous representation – new classes, new features, new connections between them – Learning how to do something better, either more efficiently or with more accuracy • previous problem solving instance (case, chain of logic) can be “chunked” into a new rule (also called memoizing) • previous knowledge can be modified – typically this is a parameter adjustment like a weight or probability in a network that indicates that this was more or less important than previously thought Types of Machine Learning • There are many ways to implement ML – Supervised vs. Unsupervised vs. Reinforcement • is there a “teacher” that rewards/punishes right/wrong answers? – Symbolic vs. Subsymbolic vs. Evolutionary • at what level