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The possibility of overfitting exists as the criteria used for training the … Which of the following is the most important when deciding on the data structure of a data mart? 8. 11. True error c. Random Variable Learning b. LMS weight update rule c. Version Space d. Consistent Hypothesis e. General Boundary f. Specific Boundary g. Concept 2. This exam is open book, open notes, but no computers or other electronic devices. 10)Differentiate between Gradient Descent and Stochastic Gradient Descent, 12)Derive the Backpropagation rule considering the training rule for Output Unit weights and Training Rule for Hidden Unit weights. Give decision trees to represent the following boolean functions. 12. Explain the important features that are required to well  define a learning problem, Explain the inductive biased hypothesis space and unbiased learner. MCQ quiz on Machine Learning multiple choice questions and answers on Machine Learning MCQ questions on Machine Learning objectives questions with answer test pdf for interview preparations, freshers jobs and competitive exams. Good luck! Explain Locally Weighted Linear Regression. Differentiate between Training data and Testing Data, Differentiate between Supervised, Unsupervised and Reinforcement Learning, Explain the List Then Eliminate Algorithm with an example, What is the difference between Find-S and Candidate Elimination Algorithm. (i) Write the learned concept for Martian as a set of conjunctive rules (e.g., if (green=Y and legs=2 and height=T and smelly=N), then Martian; else if ... then Martian;...; else Human). Anna University Chennai Syllabus 2017 Regulation- Click Here Anna University Chennai Question … 4.Discuss Entropy in ID3 algorithm with an example. The value is straightforward: If you use the most appropriate and constantly changing data sources in the context of machine learning, you have the opportunity to predict the future. 7.Explain the K – nearest neighbour algorithm for approximating a discrete – valued functionf : Hn→ V with pseudo code. are better able to deal with missing and noisy … 9.Explain CADET System using Case based reasoning. What is minimum description length principle. Explain the various issues in Decision tree Learning, 17. ;CHÃàUò5‡ âÊZ/҈™4_“šE\Ckß!½Ûv9úˆ5¾+%fF½:ùrUŠ™]àx³£}¨ºvÀSü®´³28†g±‰8J/]ïXð);(¯âHr瑎•¤cÀˆl–ìØ«Þršew–€p@D”óɝi\G­°*ÎþäJTAnûëê%€†‹eîV 'wêøÑyÀm(ž *kã¸äÁší¡²:PïˆÕs `~a@Ñø0ô+ìÏ!& T@n}–ÒŒs» What are the important objectives of machine learning… 5.Compare Entropy and Information Gain in ID3 with an example. 14)Discuss Maximum Likelihood and Least Square Error Hypothesis. Give its application. 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