2. (a) Discuss various challenges in machine learning based applications with suitable examples.

(b) Consider a learning problem where each bobinstance is described by a conjunction of n Boolean attributes a … a. Thus, a its typical instance would be Now consider a hypothesis space H in which each hypothesis is a disjunction of constraints over these attributes. For example, a typical hypothesis would be (a,T)vas-F)va-T)

Propose an algorithm that accepts a sequence of training examples and outputs a consistent hypothesis if one exists. Your algorithm should run in time that is polynomial in n and in the number of training examples

3. (b) What is the entropy of this collection of training examples with respect to the target function classification?

4. (a) Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights(w ca w_{phi} w c0 w d * c’ w d0 ), where w x0represents the threshold weight for unit x. Initialize these weights to the values\ – 1, – 1 ,*1,*1,*1\ then give their values after each of the first two training iterations of the BACKPROPAGATION algorithm. Assume learning rate eta = 0 – 3momentum alpha = 0.9 incremental weight updates, and the following training examples:

(b) Draw two-class, two-dimensional data such that (i) PCA and LDA find the same direction and (i) PCA and LDA find totally different directions.

5. (a) Suppose you test a hypothesis h and find that it commits r = 300 errors on a sample S of n = 1000 randomly drawn test examples. What is the standard deviation in errors(h)? How does this compare to the standard deviation in the example at the end of Section 5-3-4?

(b) Considering the training data provided 1010 in the following table, try to build an associative classifier model by generating all relevant association rules with support and confidence thresholds 10% and 60% respectively. Classify using this model the new example: age $30, income-medium, student=yes, credit-rating-fair, selecting the rule with the highest confidence. I would be the classification if we 8/11 to vote the class among all rules that apply?

6. (a) Draw the Bayesian belief network that represents the conditional independence assumptions of the naive Bayes classifier for the Play Tennis problem stated in the following table. Give the conditional probability table associated with the node wind

(b) What is kernel regression and noise variance? Explain in detail.67. (a) What is the lazy version of the eager decision tree learning algorithm? What are the advantages and disadvantages how of your lazy algorithm compare to La original eager algorithm?10/11What is clustering? Why do we use this in machine learning? Discuss the alds following algorithms:8aldia (i)K-means(ii)K-medoid

8. (a) How can machine learning techniques help to identify forged data recognition application? Explain the method in detail.

(b) Differentiate between supervised and unsupervised training. Explain with suitable examples.

9. Write short notes on the following:

(a) Linear Algebra for ML

(b) ANN

(c) Reinforcement Learning

**CLICK HERE TO JOIN WHATSAPP GROUP**

** Machine Learning BEU question paper solution 2022** ==

*Download*** Machine learning question paper 2022 **==>

*Download*

** ALso read= **BEU computer graphics question paper solution 2020