https://docs.google.com/document/d/1So2LFPF2RjxWKJ0mV4557Yz8mqkZPiX9/edit?usp=sharing&ouid=112711162618141236570&rtpof=true&sd=true
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...
Comments
Set-1
unit-3: 3D7
unit-4: 4D3,4D4
unit-5: 5D4, 5D9, 5D12, 5D15
Set-2
unit-3: 3D12
unit-4: 4D3, 4D11, 4D12
unit-5: 5D3, 5D15
Set-3
unit-3: 3D10, 3D15
unit-4: 4D2, 4D5
unit-5: 5D12, 5D15