https://docs.google.com/document/d/1MvO7lTdAB5gCOYGzBFcXFQWGRn0cqjyq/edit?usp=sharing&ouid=112711162618141236570&rtpof=true&sd=true
https://drive.google.com/file/d/1JTorS271MxvUxJcfZo8Zau5zK5apKMdf/view?usp=sharing
https://drive.google.com/file/d/1KR07-09HgJGX3NFUV5R3zN1MWmToPvzD/view?usp=sharing
https://drive.google.com/file/d/1Awpnodwv-TkqBzGZkiAg8O_kQn3i5ez7/view?usp=sharing
https://drive.google.com/file/d/1_-BPQIIix3uuxXPwp2uyH6fMMueTqfV_/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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MCQ's:
u-3: 8,9,10,14,6,9
U-4: 1,3,4,6,9,12,10
u-5: 2,3,12,11,1,5,14,4,7,8
FIB:
U-3:5,6,10,14,13
U-4:6,11,1,10
U-5:3,5,8,4
Match:
U-4:1
U-5:2,4,1
Descriptire:
U-3:8,10,11
U-4:4,6,7,9,5,15
U-5:1,2,3,4