- Explain artificial intelligent agents versus machine learning algorithms.
- Which issues should be kept in account to design a learning system? Mention the three features for the following learning task: Spam –nonspam Email classifier learning problem
- Mention the importance and application of the following machine learning algorithms: Find-S, Candidate-Elimination, List-Then-Eliminate, ID3, Gradient Descent and Delta Perceptron Training Rule.
- Describe training data, data mining, unsupervised and reinforcement learning.
- State restriction and preference bias. Discuss over-fitting the data problem in decision tree learning.
- Consider the House Price Prediction learning task, a linear regression problem. Now represent the appropriate training sample, hypothesis and the cost function. Also derive the general solution.
- Develop the general solution to find the MAP hypothesis assuming any suitable concept leaning task. Apply Brute-force maximum-a-posteri(MAP) leaning algorithm for the same.
- Discuss MAP and maximum-likelihood (ML) hypothesis.
- Equate biological neural network to artificial neural network (ANN).
- Design a two-input ANNs for the following Boolean operations with adjusted weights and coefficients: i) OR ii) NAND
- Clearly mention the roles of weights and activation functions in ANN. Draw various types of activation functions used in ANN.
- Mention the purposes of using the following machine learning algorithms: i) Bayes optimal classifier ii) GIBBS iii) Naïve Bayes classifier iv) Backpropagation
- Show the flow diagram for the development process of an artificial intelligent agent using ANN. Which kind of problems cannot be solved by a single layer perceptron.
- When should you use gradient descent and when normal equation method to solve a linear regression problem? Discuss in detail.

- Describe briefly the face recognition procedure applying Backpropagation feed forward ANN concept.
- Mention the significances of support vector machine (SVM) and k-nearest neighbor (KNN) classifier in machine learning.

- Discuss genetic algorithm with its operators in detail.
- Explain and design a learning system for tic-tac-toe game playing problem.
- What is machine learning? How do machine learning tools help to develop an artificial intelligent agent?

- In context of machine learning, define training data, data mining, supervised and reinforcement learning.
- When and why will you use the following learning algorithms? i) FIND-S ii) CANDIDATE-ELIMINATION iii) LIST-THEN-ELIMINATE iv) ID3 v) Gradient Descent and Delta Perceptron Training Rule

- Which issues should be kept in account to design a learning system? Mention the three features for the following learning task: Handwriting recognition learning problem
- Define restriction bias and preference bias. When does over-fitting the data problem occur in decision tree learning?
- Consider the House Price Prediction learning task, a linear regression problem. Now represent the appropriate training sample, hypothesis and the cost function. Also derive the general solution.
- Develop the general solution to find the MAP hypothesis assuming any suitable concept leaning task. Use Brute – Force MAP leaning algorithm for the same.
- Discuss maximum a posteriori and maximum likelihood hypothesis.
- Compare between biological neural network and artificial neural network (ANN).
- Design two distinct ANNs for the following operations with adjusted weights and coefficients: i) AND ii) NOR
- Mention the role of weights and activation functions in ANN. Draw various types of activation functions used in ANN.
- Show the flow diagram for the development process of an artificial intelligent agent using ANN. Which kind of problems cannot be solved by a single layer perceptron?
- Mention the purpose of using the following machine learning algorithms: i) Bayes Optimal Classifier ii) GIBBS iii) Naïve Bayes Classifier iv) Backpropagation

- When should you use gradient descent and when normal equation method to solve a linear regression problem? Discuss in details.
- Discuss and design a learning system for checkers playing problem.
- Mention the roles of support vector machine and k-nearest neighbor in machine learning.
- What is genetic algorithm? Explain the pseudo-code of the algorithm with its in details.
- Describe briefly the face recognition procedure applying feed forward ANN concept.
- What are the purpose of studying machine learning in Computer Science and Engineering?
- Define data mining, training data, supervised and unsupervised learning.
- Write the FIND-S algorithm, also mention its drawbacks.
- Which issues should be kept in mind to design a learning system?
- When does over fitting occur in decision tree learning? What are the strategies to avoid over fitting the data?

- Derive the general solution for the linear regression problem: House Price Prediction.
- Define Restriction biases and Preferences biases.
- Define Inductive bias. Give a model of inductive bias system for CANDIDATE-ELIMINATION Algorithm.
- Define Machine learning and its application areas.
- With proper example define the supervised and unsupervised learning.
- Derive the gradient descent algorithm.
- Define Decision Tree and write the ID3 algorithm.
- With an example derive the expression of Entropy measurement.
- Derive the expression of gradient descent algorithm for multi-variables.
- Define the process of assurance that the gradient descent algorithm work properly.
- Derive the expression of Logistic Regression, Linear Regression, Linear decision boundary and non-linear decision boundary.
- Explain the gradient search to maximize Likelihood in a Neural Net.
- Define the basic concept of principle Component analysis and briefly explain the PCA algorithm.
- Explain the basic concept of K-Nearest Neighbor learning.
- Define the notations of artificial neural network.
- Derive the general solution of linear regression problem with gradient descent algorithm.
- With an example explain the linear regression.
- Derive the expression of linear regression for multi-variables.
- Define genetic algorithm. Briefly explain the major operations of genetic algorithm.
- With diagram explain the steps of genetic algorithm.

- Explain the coding process to solve x – 2x + 3 = 0 using Genetic Algorithm.
- Derive the expression of cost function for logistic regression.
- With an example explain the multiclass classification problem with logistic regression.
- Derive the expression of cost function for gradient descent algorithm.
- Find out the maximum likelihood from Bayes theorem.
- What is supervised learning? How does it differ from unsupervised learning?
- When does overfitting occur in decision tree learning? What are the strategies to avoid overfitting the data.
- In which type of problems the ANN learning technique is appropriate?
- Describe biological and artificial neural network.
- What is perceptron? Give a mathematical model f a perceptron.
- Describe the various types of activation function. What are the roles of these activation functions.
- What are the attempts you have to take to alleviate the problem of local minima in Backpropagation algorithm?
- Describe briefly face recognition procedure applying neural networking concept.
- What is the role of weights in ANNs?
- What is hidden layer?