https://drive.google.com/file/d/1obrf_HXRtfAfP8rO4qP9Ztnk47fndgSG/view?usp=sharing
The field of Machine Learning (ML) is fundamentally concerned with constructing computer programs that automatically improve their performance at some task through experience. A well-posed learning problem requires identifying three key features: the class of tasks, the measure of performance to be improved, and the source of experience. The document illustrates the design of a learning system through a checkers-playing program, detailing steps like choosing the training experience, the target function (such as an evaluation function $V: B \rightarrow R$), its representation (e.g., a linear function of board features), and a function approximation algorithm like the LMS weight update rule. The design is conceptually divided into a Performance System, Critic, Generalizer, and Experiment Generator.
A core topic is Concept Learning, which involves acquiring general concepts from specific, labeled training examples. This is viewed as a search problem within a hypothesis space to find the hypothesis that best fits the training data. Algorithms like Find-S and the Candidate-Elimination Algorithm (CE-alg) are introduced for this search. The CE-alg outputs the "Version Space," which is the set of all hypotheses consistent with the training examples. The document also introduces Decision Tree Learning (DTL), a widely used method robust to noisy data and capable of learning disjunctive expressions. The ID3 algorithm, which uses Information Gain to select the best attribute for splitting at each node, is the basic DTL algorithm presented.
Key topics covered include:
A core topic is Concept Learning, which involves acquiring general concepts from specific, labeled training examples. This is viewed as a search problem within a hypothesis space to find the hypothesis that best fits the training data. Algorithms like Find-S and the Candidate-Elimination Algorithm (CE-alg) are introduced for this search. The CE-alg outputs the "Version Space," which is the set of all hypotheses consistent with the training examples. The document also introduces Decision Tree Learning (DTL), a widely used method robust to noisy data and capable of learning disjunctive expressions. The ID3 algorithm, which uses Information Gain to select the best attribute for splitting at each node, is the basic DTL algorithm presented.
Key topics covered include:
- Machine Learning (ML) Definition: The field concerned with constructing computer programs that automatically improve with experience.
- Well-Posed Learning Problems: Problems that require identifying the class of tasks, the measure of performance to be improved, and the source of experience.
- Concept Learning: The task of acquiring general concepts (approximating a boolean-valued function) from specific training examples labeled as members or non-members of the concept.
- Version Space: The subset of all hypotheses from the hypothesis space ($H$) that are consistent with the given training examples ($D$).
- Information Gain (ID3): A measure used by the ID3 decision tree learning algorithm to select the attribute that is most useful for classifying examples, characterized as the expected reduction in entropy caused by partitioning the examples according to that attribute.
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