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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https://docs.google.com/document/d/1gPPi94eLwblNzvQKr-1MQ6qltjovb7X1QgqVxK72Yak/edit?usp=sharing
important QUESTION (SET-1)
MCQ
U-3:12,14
U-4:1,3,5,7
U-5:2,4,8,10
FIB
U-3:11,13
U-4:1,6,10
U-5:2,6
MTF
U-3:2
U-4:1
Q&A:
U-3:11
U-4:1,7,9
U-5:2,6
Important QUESTION (SET-2)
MCQ
U-3:13,15
U-4:2,4,8
U-5:3,5,9
FIB
U-3:12,14
U-4:3,7
U-5:1,7
MTF
U-3:1
U-4:2
Q&A
U-3:9,3
U-4:2,10
U-5:4,9