https://drive.google.com/file/d/1MOrIqMQGg7WNtbq3CXh4h8OAkzKue29t/view?usp=sharing
https://drive.google.com/file/d/1B7jEhJSewTrJTGNrpM6iBCaehjKd-W4L/view?usp=sharing
The document provides a comprehensive overview of fundamental Machine Learning concepts, beginning with an introduction to the field and the definition of well-posed learning problems, characterized by a task (T), a performance measure (P), and experience (E). It details the design of a learning system, using the checkers game as an example, covering choices for training experience, target function representation, and approximation algorithms like the LMS training rule. The text then dives into Concept Learning, explaining how it can be viewed as a search problem and introducing algorithms like FIND-S and Candidate-Elimination, emphasizing the concept of a Version Space. Furthermore, it explores Decision Tree Learning with the ID3 algorithm, detailing the use of Entropy and Information Gain to construct trees, and addresses issues like overfitting and alternative attribute selection measures. Finally, the document covers Artificial Neural Networks, including Perceptrons, Gradient Descent, and the BACKPROPAGATION algorithm for multilayer networks, along with an introduction to Bayesian Learning and Instance-Based Learning.
Here are 5 key bullet points of the specific topics covered with a brief definition for each:
Here are 5 key bullet points of the specific topics covered with a brief definition for each:
- Well-Posed Learning Problem: A problem defined by three features: a class of tasks (T), a measure of performance (P), and a source of experience (E), where the performance at tasks in T, as measured by P, improves with experience E.
- Version Space: The subset of hypotheses from the hypothesis space (H) that are consistent with the given training examples (D).
- Information Gain: A statistical property used in the ID3 algorithm that measures the expected reduction in Entropy (impurity) caused by partitioning the training examples according to a given attribute, helping select the best attribute for a node.
- BACKPROPAGATION Algorithm: An algorithm that learns the weights for a multilayer neural network using gradient descent to minimize the squared error between the network output values and the target values.
- Q-Learning: A basic form of Reinforcement Learning that uses Q-values (action values) to iteratively improve the behavior of a learning agent by estimating the expected cumulative reward for taking an action in a given state.
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