1. What is learning?
Learning takes many forms, depending on the nature of the performance element, the component to be improved, and the available feedback.
2. What are the types of Machine learning?
3. Define the following:
Learning a discrete-valued function is called classification.
Learning a continuous function is called regression.
4. What is inductive learning?
The task is to learn a function from examples of its inputs and outputs is called inductive learning.
5. When will a learning problem is said to be realizable or unrealizable?
A hypothesis space consisting of polynomials of finite degree represent sinusoidal functions accurately, so a leaner using that hypothesis space will not be able to learn from sinusoidal data.
A Learning process is realizable if the hypothesis space contains the true function, otherwise it is unrealizable.
6. What is decision tree?
A decision tree takes as input an object or situation described by a set of attributes and returns a “decision”, the predicted output value for the input. The input
can be discrete or continuous.
7. Define goal predicate.
To define the goal predicate should have the following list of attributes.
j) Wait Estimate
8. Define the kinds of functions. (Apr/May 2005)
The kinds of functions are a real problem.
Returns 1 if and only if an even number of inputs are 1, then an exponentially large decision tree will be needed.
Returns 1 if more than half of its inputs are 1.
9. Define training set.
The positive examples are the ones in which the goal WillWait is true (X1,
X3,…….); the negative examples are the ones in which it is false (X2, X5,…..). The
complete set of examples is called the training set.
10. How do you assess the performance of the learning algorithm?
A learning algorithm is good if it produces hypotheses that do a good job of predication the classifications of unseen examples. We do this on a set of examples known as the test set. It is more convenient to adopt the following methodology:
a) Collect a large set of examples.
b) Divide it into two disjoint sets: the training set and the test set.
c) Apply the learning algorithm to the training set, generating a hypothesis h.
d) Measures the percentage of examples in the test set that are correctly classified by h.
e) Repeat steps 1 to 4 for different sizes of training sets and different randomly selected training sets of each size.
11. Define Overfitting.
Whenever there is a large set of possible hypotheses, one has to be careful not to use the resulting freedom to find meaningless “regularly” in the data. This
problem is called overfitting.
12. What is ensemble learning?
The idea of ensemble learning methods is to select a whole collection, or ensemble, of hypotheses from the hypothesis space and combine their predictions.
13. Define Weak learning algorithm.
If the input learning algorithm L is a week learning algorithm which means that L always returns a hypothesis with weighted error on the training set that is slightly better than random guessing.
14. Define computational learning theory. (Apr/May 2008)
The approach taken in this section is based on computational learning theory, a field at the intersection of AI, statistics, and theoretical computer science.
15. What do you mean by PAC-learning algorithm? (Apr/May 2008)
Any learning algorithm that returns hypothesis that are probably approximately correct is called a PAC-learning algorithm.
16. What is an error?
The error of a hypothesis h with respect to the true function f given a distribution D over the examples as the probability that h is different from f on an example.
Error (h) = p (h(x) = f(x)|x drawn from D)
17. Define Sample Complexity.
The number of required examples, as a function of E and, is called the sample complexity of the hypothesis space.
18. Define neural networks.
A neuron is a cell in the brain whose principal function is the collection, processing and dissemination of electrical signals.
19. Define units in neural networks.
Neural networks are composed of nodes or units connected by directed links. A link from unit j to unit i serve to propagate the activation aj from j to i.
20. Mention the types of neural structures.
a. Feed-forward networks
b. Cyclic or recurrent network
21. Define epoch.
Each cycle through the examples is called an epoch. Epochs are repeated until some stopping criterion is reached typically that the weight changes have become very small.
22. What do you mean by Bayesian learning? (Nov/Dec 2005)
Bayesian learning methods formulate learning as a form of probabilistic inference, using the observation to update a prior distribution over hypotheses. This approach provides a good way to implement Ockham’s razor, but quickly becomes intractable for complex hypothesis spaces.
23. What is reinforcement?
The problem is this without some feedback about what is good and what is bad, the agent will have no grounds for deciding which move to make. The agent needs to when it loses. This kind of feedback is called a reward, or reinforcement.
24. Define passive learning.
The agent’s policy is fixed and the task is to learn the utilities of states, this
could also involve learning a model of the environment.
25. Define the following:
a. Utility-based agent: Learns a utility function on states and uses it to select actions that maximize the expected outcome utility.
b. Q-learning agent: Learns an action-value function or Q-function, giving the expected utility of taking a given action in a given state.
c. Reflex agent: Learns a policy that maps directly from states to actions.
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