AI and Machine Learning in Power Electronics — Winter 2021

Define version space, candidate eliminations and inductive bias.

How the knowledge should be represented in AI techniques?

Compare supervised, unsupervised and reinforcement machine learning methods.

Define state space search.

What are the issues in design of search programs?

Explain steepest ascent hill climbing algorithm.

Explain convert to clause form algorithm.

Differentiate depth first search and breadth first search algorithm.

What problems is Artificial Intelligence helping to solve for Samsung company?

How does Alibaba use artificial intelligence?

Explain computable functions and predicates.

What are the key challenges and learning points for the Dominos using AI?

Explain AO* algorithm for problem reduction.

Draw neural representation of AND gate.

Write prolog program to find maximum number from list.

Explain Decision tree algorithm with issues.

Describe radial basis function.

Explain process of weights change during training in multilayer network.

Explain K- Nearest Neighbors algorithm.

Define Hypothesis Space.

Let hypothesis space as three function h1,h2 and h3. P(h1|D)=0.4, P(h2|D)=0.3, P(h3|D)=0.3. For new instance x, h1(x)=+1, h1(x)=+1, h1(x)= -1. What is most probable classification of x?

Explain Naïve Bayes algorithm.

Give an advantages of Gibbs algorithm over Bayes optimal classifier.

Describe relationship between Maximum Likelihood and Least squared Error Hypothesis.

Explain Minimum Description Length Principle.