https://drive.google.com/file/d/1HrvrsBfZ8CA9nJk8Vav93IHqZetWZ8LO/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
Unit-1:2,4,6,15-,-3,4,5,11
Unit-2:2,4,11,15-,-3,4,5,12
Unit-3:1,2,3
FIB:
Unit-1:1,4,10,14
Unit-2:4,5,6,12
Unit-3:1,6
MFQ:
Unit-1:2,5
Unit-2:1,2
Descriptive Questions:
Unit-1:1,2,4,7,14,14
Unit-2:1,7,11,7
Unit-3:2,3
set2
Unit-3
MCQ:8,15
FIB:10
MTF:-
DQ:7,12
unit-4
MCQ:1,4,5,12
FIB:3,5,9
MTF:5
DQ:5,10
unit-5
MCQ:1,2,9,10
FIB:1,10
MTF:4
DQ:13,15
Set-1
Description question
U-3
10
U-4
6,11,13
U-5
13,15
https://notebooklm.google.com/notebook/e73fa5cd-2100-4cf7-ae65-2eb262334d5c