https://drive.google.com/file/d/1OZQA2NHm_NjVJOORVY-n2oVjavXq2EQD/view?usp=sharing
https://drive.google.com/file/d/1KqB9T1NIw2Uud-E6auOdPVDK1lKoszht/view?usp=sharing
https://drive.google.com/file/d/1GNWLV2QbdbONqBBXwr2T9FJhdrsvXH2x/view?usp=sharing
ML unit-1 Notes
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 train...
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