https://docs.google.com/document/d/11mQYK3hG82kDEV1ad_HOXwS3dLlbG8R9/edit?usp=sharing&ouid=112711162618141236570&rtpof=true&sd=true
https://drive.google.com/file/d/17i6C1PRkd1QmxVNoyO81uqRhxMnFFd8V/view?usp=sharing
https://docs.google.com/document/d/1RYkFUvfun-DewJ9Iptg8dYFz9vEFa-qS/edit?usp=sharing&ouid=112711162618141236570&rtpof=true&sd=true
https://drive.google.com/file/d/1GFAzlY7jcxnaHbzD33V37uLQii9NEQiY/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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Unit-3
MCQ: 6,7
Fill ups: 11,15
Questions: 14,15
Unit-4
MCQ: 2,6,7,8
Fill ups: 5,13
match: 2
Questions:3,6
Unit:5
MCQ: 3,5,8
Fill ups: 3,11
match: 3
Questions: 7,10
Unit-3
MCQ: 8,14
Fill ups: 14
Questions: 10
Unit-4
MCQ: 4,6,8,13
Fill ups: 4,6,13
match: 2
Questions:4,8,10
Unit:5
MCQ: 4,9,12,11
Fill ups: 5,11
match: 2
Questions: 3,13
unit 3- 10
unit4- 3,8,13,6
unit5- 4,14