SAQs

1. Write short notes on PAC learning.

What is PAC Learning?

PAC (Probably Approximately Correct) learning is a framework in learning theory that provides a mathematically rigorous way of analyzing and designing machine learning algorithms. It was introduced by Leslie Valiant in 1984.

Key Concepts

• Hypothesis class: A set of possible concept functions (hypotheses) that can be learned by the algorithm.
• Sample complexity: The number of training examples required to achieve a certain level of accuracy.
• Error bound: An upper bound on the difference between the true error and the empirical error of the learned hypothesis.
• Confidence: A parameter that controls the trade-off between accuracy and sample complexity.

How PAC Learning Works

1. Choose a hypothesis class: Select a set of possible concept functions that the algorithm can learn.
2. Collect a random sample: Gather a set of training examples drawn randomly from the underlying distribution.
3. Learn a hypothesis: Choose a hypothesis from the hypothesis class that best fits the training data.
4. Evaluate the learned hypothesis: Calculate the empirical error of the learned hypothesis on the training data.
5. Prove bounds: Show that the learned hypothesis is probably approximately correct, with high probability.

2. What Factors contribute to the popularity of Genetic Algorithm?

Reasons for Popularity

Genetic algorithms (GAs) are a popular optimization technique that have been widely used in a variety of fields. Some of the key factors that contribute to their popularity include:

• Easy to implement: GAs are relatively simple to implement, making them accessible to researchers and practitioners without extensive expertise in optimization.