机器学习课程教案.docVIP

Machine Learning Course Plan Lecture One Title: Introduction Content: Basic information about this course: books, TA, office, homework, project and test form… Introduce the definition of learning systems. Give an overview of applications to show the goals of machine learning. Introduce the aspects of developing a learning system: training data, concept representation, function approximation. Targets: Understand the background of machine learning; Remember the basic function of machine learning; Get the general ideas of machine learning’s problem and point; Processions: What is Machine Learning? Applications of ML Disciplines relevant to ML Well-Posed Learning Problems Designing a Learning System Perspectives and Issues In Machine Learning How To Read This Book Difficulties: How to design a learning system; The understand of concept. Lecture Two Title: Concept Learning and the General-to-Specific Ordering Contents: What is the concept learning task? Where is it applied to? Make students understand concept learning is equivalent to search through a hypothesis space. Illustrate step by step the procedure of general-to-specific ordering of hypotheses, to find the maximally specific hypotheses Targets: Understand the background of concept learning; Remember the basic concept of concept learning, version space, etc; Processions: Introduction A Concept Learning Task Concept Learning as Search Find-S：Finding a Maximally Specific Hypothesis Difficulties: What’s the process of concept learning; Remember the concept of concept learning; Lecture Three Title: Candidate elimination and Inductive bias Contents: Introduce the definition of version spaces and the candidate elimination algorithm. How to learning conjunctive concepts? Introduce and emphasize the importance of inductive bias. Targets: Remember the process of candidate elimination algorithm; Get the basic idea of the useless of unbiased learning; Processions: Version Spaces and the Candidate-Elimination