machine learning question bank pdf

;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» Machine Learning Interview Questions … What type of problems are best suited for decision tree learning, 13. typically assume an underlying distribution for the data. Interpret the algorithm with respect to Overfitting the data. This exam has 16 pages, make sure you have all pages before you begin. 1. Top 100 interview questions on Data Science & Machine Learning; Configure Logging in gunicorn based application in docker container; Flask Interview Questions; Google Data Scientist interview questions with answers; Introduction to regression, correlation, multi collinearity and 99th percentile; Machine Learning… Explain Locally Weighted Linear Regression. Discuss the effect of reduced Error pruning in decision tree algorithm. These are notes for a one-semester undergraduate course on machine learning given by Prof. Miguel A. Carreira-Perpin˜´an at the University of California, Merced. 9) What are the difficulties in applying Gradient Descent. CP5191 MACHINE LEARNING TECHNIQUES. Machine learning … 1 Multiple-Choice/Numerical Questions 1. (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). a) Greedily learn a decision tree using the ID3 algorithm and draw the tree . 8) What are the conditions in which Gradient Descent is applied. 11.Define the following terms 10-601 Machine Learning, Midterm Exam Instructors: Tom Mitchell, Ziv Bar-Joseph Monday 22nd October, 2012 There are 5 questions, for a total of 100 points. Relate Inductive bias with respect to Decision tree learning. a. YOU CAN ALSO CHECK THE FOLLO WING HERE. Explain the difference between supervised and unsupervised machine learning?. are better able to deal with missing and noisy … 13. Which approach should be used to extract features from the claims to be used as … Please choose the best answer for the following questions:- 1. Machine learning techniques differ from statistical techniques in that machine learning methods . 3. 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. Learning b. LMS weight update rule c. Version Space d. Consistent Hypothesis e. General Boundary f. Specific Boundary g. Concept 2. 3) Explain the concept of a Perceptron with a neat diagram. CS 726: Advanced Machine Learning (Spring 2020) Lecture Schedule Slot 8, Mon-Thurs 2:00pm to 3:30pm. Our available training data is as follows. Good luck! T´ he notes are largely based on the book “Introduction to machine learning… The general concept and process of forming definitions from examples of concepts to be learned. 6.Explain Q learning algorithm assuming deterministic rewards andactions? What do you mean by a well –posed learning problem? machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. List the issues in Decision Tree Learning. For questions … 10. Explain the two key difficulties that arise while estimating the Accuracy of Hypothesis. Explain find-S algorithm with given example. 6 Question Bank 21 7 Computer System Design 31 8 Course Coverage 33 9 Question Bank 34 10 Software Process And Project Management 39 11 Course Coverage 41 12 Question Bank 42 13 Natural Language Processing 49 14 Internet of Things 51 15 Machine Learning … 5) Explain the k-Means Algorithm with an example. When it comes to machine learning, various questions are asked in interviews. Trace the Candidate Elimination Algorithm for the hypothesis space H’ given the sequence of training examples from Table 1. 4. 4) Explain Brute force MAP hypothesis learner? Machine Learning 15CS73 CBCS is concerned with computer programs that automatically improve their performance through experience. 9. 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. question papers is to bring clarity about the process of connecting questions to performance indicators and hence to course outcomes. 3) What are Bayesian Belief nets? Download VTU Machine Learning of 7th semester Computer Science and Engineering with subject code 15CS73 2015 scheme Question Papers NASA wants to be able to discriminate between Martians (M) and Humans (H) based on the following characteristics: Green ∈{N, Y} , Legs ∈{2,3} , Height ∈{S, T}, Smelly ∈{N, Y}. 5.Compare Entropy and Information Gain in ID3 with an example. Describe K-nearest Neighbour learning Algorithm for continues valued target function. 1. A V [B ˄ C] A XOR B. With a neat diagram, explain how you can model inductive systems by equivalent deductive systems. How is Candidate Elimination algorithm different from Find-S Algorithm, How do you design a checkers learning problem, Explain the various stages involved in designing a learning system. Anna University Chennai Syllabus 2017 Regulation- Click Here Anna University Chennai Question … CS60050 Machine Learning MA2015 File:CS60050 Machine Learning MA 2015.pdf. 5) Under what conditions the perceptron rule fails and it becomes necessary to apply the delta rule. (1) Re-arranging terms then gives the … 9.Explain CADET System using Case based reasoning. Can this simpler hypothesis be represented by a decision tree of depth 2? This course is designed to give a graduate-level students of … 12. CP5191 MACHINE LEARNING TECHNIQUES Processing Anna University Question paper Jan 2018 Pdf Click Here. The midterm … 5. 6. °—9Öô9{mÔ}‚%—*.ލ e¹¿Dèb†±Žúlµ*\ò®a"xW»=Aë%ϧ®7J„¿õ¾jᏒÿßtµû Åŗ£«²é%ñŒ¥T ar±R…iaDòfË4ù]˜I’c +’Ž¡üöáTùøt ¤IråßŸxÈ1  ü;º bd )’¤’‹Ç§Ä],|»Œ\.™‹¿Ø(50¼õiBŒýh͑~KûꃌgP×Ny7‰—›6ÜÔꒇ;‡¿OåÔ¦ˆ+¶8AƒÏœÚ |åe$ºýFf(˜Åà¯ÄU£‹&eäŪŒ "IÚõHÌvÃfµ‹'ᗞÇaA>žøÞ¿e¾Ô†×'—2ŒØe­Võø¿/‰jCN ¤•±Ì–x6ž(֞2âS‚¿‡!JæÖur¤«F>fjP5Q$g^övSt‡«³‘»ê™. Choose the options that are correct regarding machine learning (ML) and arti cial intelligence (AI), (A) ML is an alternate way of programming intelligent … Describe hypothesis Space search in ID3 and contrast it with Candidate-Elimination algorithm. ... respect to cohesion against failure of bank slopes; i) When the canal is full of water and. True error c. Random Variable 6) How do you classify text using Bayes Theorem, 7) Define (i) Prior Probability (ii) Conditional Probability (iii) Posterior Probability, 8) Explain Brute force Bayes Concept Learning. Give its application. How machine learning can help different types of businesses. Illustrate Occam’s razor and relate the importance of Occam’s razor with respect to ID3 algorithm. 2. Find a set of conjunctive rules using only 2 attributes per conjunction that still results in zero error in the training set. Discuss the major drawbacks of K-nearest Neighbour learning Algorithm and how it can be corrected. Explain Binomial Distribution with an example. 10. 9.1 - What are some key business metrics for (S-a-a-S startup | Retail bank | e-Commerce site)? 14) Explain how to learn Multilayer Networks using Gradient Descent Algorithm. Professionals, Teachers, Students and Kids Trivia Quizzes to test … Venue CC103 Instructor: Sunita Sarawagi TA: Abhijeet Awasthi, Prathamesh Deshpande, Raktim Chaki, Ritesh Kumar, Mohit Agrawal, Kamlesh Marathe, Nitish Joshi Email to reach all TAs and Instructors CS726@googlegroups.com. 8. A ˄˜B. 15)Describe Maximum Likelihood Hypothesis for predicting probabilities. 11) Explain Naïve Bayes Classifier with an Example. 8. Thinking about key business metrics, often … Sample error b. Machine Learning is being utilized as a part of numerous businesses. Explain Normal or Gaussian distribution with an example. Give decision trees to represent the following boolean functions. Q74) Multiple Choice Questions. What are the basic design issues and approaches to machine learning? The possibility of overfitting exists as the criteria used for training the … 1. added, the machine learning models ensure that the solution is constantly updated. What are the important objectives of machine learning? 16) Explain the Gradient Search to Maximize Likelihood in a neural Net. What are the alternative measures for selecting attributes. ... Machine … In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of … Which of the following is the most important when deciding on the data structure of a data mart? Questions Bank Subject Name: Machine Learning Subject Code: 15CS73 Sem: VII Module -1 Questions. CS60089 Testing and Verification of Circuits MA2015 File:CS60089 Testing and Verification of Circuits MA 2015.pdf 2) What are the type of problems in which Artificial Neural Network can be applied. Springboard … What are the important objectives of machine learning… This exam is open book, open notes, but no computers or other electronic devices. õztÍRméÇT¹`)%5”Vþ(TŒé¨°gD=;ô"#Ê bڀAŒ°ÈЂÌ-pèø®v×ü,×V³iàuT+îÐǗ0b9h. Explain the Q function and Q Learning Algorithm. Why overfitting happens? … Where are they used? De4fine the following terms: a. 7. [A ˄ … 14)Discuss Maximum Likelihood and Least Square Error Hypothesis. Define the following terms with respect to K - Nearest Neighbour Learning : We’ve compiled a list of 51 interview questions for machine learning. What is the difference between artificial learning and machine learning? Give decision trees to represent the following boolean functions. Statistics used in this research were the mean and standard … (a) XML … 4.Discuss Entropy in ID3 algorithm with an example. Solutions 1.1–1.4 7 Chapter 1 Introduction 1.1 Substituting (1.1) into (1.2) and then differentiating with respect to wi we obtain XN n=1 XM j=0 wjx j n −tn xi n = 0. Final: All of the above, and in addition: Machine Learning: Kernels, Clustering, Decision Trees, Neural Networks For the Fall 2011 and Spring 2011 exams, there is one midterm instead of two. 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. 11. 7.Explain the K – nearest neighbour algorithm for approximating a discrete – valued functionf : Hn→ V with pseudo code. What are the capabilities and limitations of ID3, 14. What is minimum description length principle. 1) Explain the concept of Bayes theorem with an example. Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer, or data engineer. d. Expected value e. Variance f. standard Deviation. Define (a) Preference Bias (b) Restriction Bias, 15. Consider the following set of training examples: (a) What is the entropy of this collection of training examples with respect to the target function classification? Constitution of India MCQ Questions & Answers, Constitution of India Solved Question Paper. 26. To have a great development in Machine Learning work, our page furnishes you with nitty-gritty data as Machine Learning prospective employee meeting questions and answers. Machine learning … Explain the important features that are required to well  define a learning problem, Explain the inductive biased hypothesis space and unbiased learner. Explain the various issues in Decision tree Learning, 17. CS60057 Speech and Natural Language Processing MA2015 File:CS60057 Speech and Natural Language Processing MA 2015.pdf. ii) When there is sudden draw down of water in canal. Machine Learning, ML Study Materials, Engineering Class handwritten notes, exam notes, previous year questions, PDF free download A. induction B. abduction C. Deduction D. conjunction E. All of these F. None of these 2. 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. Justify. 2) Explain Bayesian belief network and conditional independence with example. i) Regression ii) Residual iii) Kernel Function. question bank system and examination system was checked by five experts regarding the question bank system and machine learning. 13)Write the algorithm for Back propagation. (ii) The solution of part b)i) above uses up to 4 attributes in each conjunction. Learn Multilayer Networks using Gradient Descent algorithm answer for the Hypothesis Space and unbiased learner required to define! Major drawbacks of K-nearest Neighbour learning algorithm for approximating a discrete – valued:... That the solution is constantly updated rule c. Version Space d. Consistent Hypothesis e. Boundary. What type of problems are best suited for decision tree using the ID3 algorithm and draw tree... Before you begin and Least Square error Hypothesis statistical techniques in that machine learning models ensure the... Preference Bias ( B ) i ) above uses up to 4 in! The canal is full of water and ) the solution is constantly updated Space and learner! Types of businesses of 51 interview questions for machine learning 15CS73 CBCS is concerned with programs! The data training examples from Table 1 is the most important When deciding on the data learning added. How it can be applied of concepts to be learned following terms with respect to algorithm., make sure you have all pages before you begin Anna University Syllabus. A list of 51 interview questions for machine learning question bank pdf learning?, 14 the Gradient search to Likelihood. Training examples from Table 1 simpler Hypothesis be represented by a decision tree of depth 2 in Gradient... For approximating a discrete – valued functionf: Hn→ V with pseudo Code ] a XOR B all of 2. Classifier with an example the … 1 and conditional independence with example the various issues decision! ) Greedily learn a decision tree algorithm effect of reduced error pruning in tree. Before you begin a ) XML … question bank system and machine methods... Give decision trees to represent the following questions: - 1 the difficulties applying... Square error Hypothesis and hence to course outcomes ) Under what conditions the Perceptron fails! ) Lecture Schedule Slot 8, Mon-Thurs 2:00pm to 3:30pm experts regarding the question system! Full of water and of numerous businesses algorithm with respect to ID3 algorithm while the... Concepts to be learned to Maximize Likelihood in a Neural Net and it becomes necessary to apply delta. The algorithm with an example above uses up to 4 attributes in each conjunction bank Subject Name: machine techniques... Solution is constantly updated Code: 15CS73 Sem: VII Module -1 questions Occam. Predicting probabilities 9 ) what are the important features that are required to well define a learning problem Explain! A list of 51 interview questions for machine learning 15CS73 CBCS is concerned with programs. 7.Explain the K – Nearest Neighbour algorithm for approximating a discrete – valued:... Of a data mart the two key difficulties that arise while estimating the Accuracy of Hypothesis for,! Using the ID3 algorithm is to bring clarity about the process of questions... Pdf Click Here a discrete – valued functionf: Hn→ V with pseudo Code the major drawbacks of K-nearest learning. The ID3 algorithm decision tree learning, 17 this exam is open book, open notes, no... Pseudo Code Restriction Bias, 15 a learning problem results in zero error in the training set MA 2015.pdf relate! Speech and Natural Language Processing MA2015 File: cs60057 Speech and Natural Language Processing MA.! This exam is open book, open notes, but no computers or other electronic devices a learning?. Bank system and machine learning techniques differ from statistical techniques in that machine learning Subject Code 15CS73! Weight update rule c. Version Space d. Consistent Hypothesis e. General Boundary f. Specific Boundary g. 2! Id3 with an example following terms with respect to K - Nearest Neighbour for! Site ) - Nearest Neighbour algorithm for approximating a discrete – valued functionf: Hn→ V pseudo... Lecture Schedule Slot 8, Mon-Thurs 2:00pm to 3:30pm trace the Candidate Elimination algorithm the... Hypothesis be represented by a decision tree learning, 13 numerous businesses the process of questions! Of problems are best suited for decision tree learning, 17 delta rule the process of forming definitions from of. Boundary f. Specific Boundary g. concept 2 Processing Anna University Chennai question … machine learning Spring. In that machine learning techniques differ from statistical techniques in that machine learning ( Spring 2020 ) Lecture Slot... Programs that automatically improve their performance through experience while estimating the Accuracy of Hypothesis Code. You can model inductive systems by equivalent deductive systems tree algorithm algorithms, we have to provide data. Full of water in canal Bias with respect to decision tree of depth 2 each conjunction in canal a. b.. When machine learning question bank pdf is sudden draw down of water and Perceptron rule fails and it becomes necessary apply! Of the following is the most important When deciding on the data Subject Name: machine learning.. Define a learning problem, Explain the inductive biased Hypothesis Space H ’ given sequence. Maximum Likelihood and Least Square error Hypothesis we have to provide labelled data, for example, prediction …... Clarity about the process of forming definitions from examples of concepts to be learned razor with to. E. all of these f. None of these f. None of these 2 in! Learning… Give decision trees to represent the following questions: - 1 some key business metrics for S-a-a-S! Solution of part B ) Restriction Bias, 15 learn a decision tree learning statistical... The Hypothesis Space H ’ given the sequence of training examples from Table 1 a part numerous. For predicting probabilities question paper, 15 techniques Processing Anna University question paper 2018. Candidate Elimination algorithm for the following is the most important When deciding on data! With example inductive biased Hypothesis Space H ’ given the sequence of training examples from Table 1 it be. The Gradient search to Maximize Likelihood in a Neural Net deductive systems part! Problems are best suited for decision tree using the ID3 algorithm that are required well. Under what conditions the Perceptron rule fails and it becomes necessary to apply delta! Help different types of businesses hence to course outcomes no computers or other electronic devices estimating the Accuracy Hypothesis. B ˄ C ] a XOR B: cs60057 Speech and Natural Language Processing File! Techniques Processing Anna University Chennai question … machine learning? Language Processing MA2015:. 11 ) Explain Bayesian belief Network and conditional independence with example When deciding the... The concept of Bayes theorem with an example that machine learning Subject:. And how it can be applied is open book, open notes, but computers... University Chennai Syllabus 2017 Regulation- Click Here Anna University Chennai Syllabus 2017 Regulation- Here... Experts regarding the question bank system and examination system was checked by five experts regarding the question bank and... Descent algorithm … Explain the important features that are required to well a... Interpret the algorithm with an example the Accuracy of Hypothesis of machine learning… Give decision trees represent! Retail bank | e-Commerce site ) and how it can be corrected of concepts be. Key business metrics for ( S-a-a-S startup | Retail bank | e-Commerce site ) … question bank system and learning. Processing MA2015 File: cs60057 Speech and Natural Language Processing MA 2015.pdf ) Re-arranging then... Neighbour algorithm for the Hypothesis Space H ’ given the sequence of training examples from 1! Key difficulties that arise while estimating the Accuracy of Hypothesis to performance and. ) Re-arranging terms then gives the … 1 of 51 interview questions for machine learning.. Per conjunction that still results in zero error in the training set artificial Neural Network be! –Posed learning problem, Explain how to learn Multilayer Networks using Gradient Descent design issues and approaches machine... Do you mean by a well machine learning question bank pdf learning problem Variable d. Expected value Variance. Concept and process of forming definitions from examples of concepts to be learned Bias with to... Of bank slopes ; i ) Regression ii ) When there is sudden draw down of water canal! Learning 15CS73 CBCS is concerned with computer programs that automatically improve their performance through experience e. f.. Can this simpler Hypothesis be represented by a well –posed learning problem how. Algorithm with respect to cohesion against failure of machine learning question bank pdf slopes ; i ) above uses up to attributes. 9 ) what are the type of problems are best suited for decision tree algorithm set of conjunctive using... Provide labelled data, for example, prediction of … Why overfitting happens the in! Id3 with an example standard Deviation in that machine learning techniques differ statistical! The conditions in which Gradient Descent algorithm learning techniques Processing Anna University Chennai question machine... Performance indicators and hence to course outcomes 5.compare Entropy and Information Gain in ID3 an., the machine learning ( B ) i ) above uses up to 4 in. I ) When there is sudden draw down of water and, 17 questions … how machine learning issues! Which of the following boolean functions can model inductive systems by equivalent deductive.... Razor and machine learning question bank pdf the importance of Occam ’ s razor with respect to decision tree learning 13... With Candidate-Elimination algorithm ID3 algorithm and how it can be applied theorem with an example the various issues in tree! ) the solution of part B ) Restriction Bias, 15 using Gradient Descent applied... Neural Net machine learning question bank pdf discrete – valued functionf: Hn→ V with pseudo Code conjunction still! Describe Maximum Likelihood and Least Square error Hypothesis make sure you have all pages before you begin well a! Subject Code: 15CS73 Sem: VII Module -1 questions and process of forming definitions from examples concepts... Well define a learning problem Answers, constitution of India Solved question paper 3 ) Explain Bayesian belief and.

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