Model Question Papers
28-Nov-2018 Develop an autonomous system using reinforcement learning. 5. Evaluate various machine learning algorithms and build a solution for real-world.
10-601 Machine Learning Midterm Exam
18-Oct-2012 Solution: Yes k-means assigns each data point to a unique cluster based on its distance to the cluster center. Gaussian mixture clustering ...
SRM VALLIAMMAI ENGINEERING COLLEGE (An Autonomous
PART – A. Q.No. Questions. BT Level. Competence. 1. Define Machine Learning. BTL 1. Remembering. 2. Compare learning vs programming. BTL 2. Understanding.
QUESTION BANK MALLA REDDY COLLEGE OF ENGINEERING
Describe Train Model using Machine Learning Algorithm Test model. (10M ) Answer any one full question from each unit. Each question carries 10 marks and ...
Untitled
Question Paper is in English language. Candidate can answer in English "I am learning machine learning using Python". Import the required libraries.
TEACHERS RECRUITMENT BOARD Post Graduate Computer
23-Jun-2019 Question Paper – 23.06.2019. 1. 1. It is a class of machine learning techniques that make use of both labelled and unlabelled examples where ...
Few-Shot Complex Knowledge Base Question Answering via Meta
The problem we study in this paper is transform- ing a complex natural-language question into a sequence of actions i.e.
department of skill education - artificial intelligence (subject
Sample Question Paper for Class X (Session 2022-2023). Max. Time: 2 Hours. Max Answer any 3 out of the given 5 questions on Employability Skills (2 x 3 = 6 ...
CLINIQA: A Machine Intelligence Based Clinical Question
Question Answering system developed for medical practitioners ... In this paper we presented a novel implementation of machine learning based clinical question.
SAMPLE QUESTION PAPER 1 CLASS X ARTIFICIAL
Unscramble the letters and find the correct answer. (1). Machine Learning + ______ = Artificial Intelligence. (i) TRNUALA GNLAGAUE CPSISEROGN. (ii)
Model Question Papers
11-Dec-2018 Evaluate various machine learning algorithms and build a solution for real-world applications. Model Question Paper.
Deep Learning for Question Answering
Deep Learning for Question Answering. Mohit Iyyer Briefly: deep learning + NLP basics ... Answers can appear as part of question text (e.g. a.
10-601 Machine Learning Midterm Exam
18-Oct-2012 Circle the correct answer(s). (h) [3 points] As the number of training examples goes to infinity your model trained on that data will have:.
Learning When Not to Answer: a Ternary Reward Structure for
In this paper we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications
Automatic Question Paper Generation using ML: A Review
We allude the taking after paper with respect to machine learning This location can allow all questions' answers but in organizing they got to take ...
Automated Paper Evaluation System for Subjective Handwritten
Model answers will be provided by the teacher and a machine learning model will be trained. The teacher will also provide. Page 2. keywords QST (question
A Machine Learning-Based Approach to Predicting Success of
12-Feb-2013 This study creates a model to predict question failure or a question that does not receive an answer
A reinforcement learning formulation to the complex question
We use extractive multi-document summarization techniques to perform complex ques- tion answering and formulate it as a reinforcement learning problem.
DEEP LEARNING B.TECH-IT VIII SEM QUESTION BANK Question
Question - What are the applications of Machine Learning .When it is used. Answer - Artificial Intelligence (AI) is everywhere.
DEEP LEARNING APPROACHES FOR ANSWER SELECTION IN
In paper [10] author evaluates the performance of proposed question answering system model with a database which consists of pair of questions and answers. AT&T
[PDF] EXAMPLE Machine Learning Exam questions
EXAMPLE Machine Learning (C395) Exam Questions (1) Question: Explain the principle of the gradient descent algorithm Accompany
[PDF] 10-601 Machine Learning Midterm Exam
18 oct 2012 · 10-601 Machine Learning Midterm Exam answer each of these true/false questions and explain/justify your answer in no more than 2
Machine Learning Question With Answers Module 1 - VTUPulse
MODULE 1 – INTRODUCTION AND CONCEPT LEARNING 1 Define Machine Learning Explain with examples why machine learning is important 2 Discuss some applications
[PDF] Questions Bank
What are the basic design issues and approaches to machine learning? 7 How is Candidate Elimination algorithm different from Find-S Algorithm 8 How do you
[PDF] 1924103-machine-learningpdf
SRM Nagar Kattankulathur – 603 203 DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK I SEMESTER – M Tech - Data Science 1924103– MACHINE LEARNING
[PDF] QUESTION BANK MALLA REDDY COLLEGE OF ENGINEERING
Part A is compulsory which carriers 25 marks and Answer all questions h Discuss machine learning algorithm in the context of multiple analytical
Previous year question paper for ML (BE Information Technology
Machine Learning Previous year question paper with solutions for Machine Learning from 2020 to 2021 Our website provides solved previous year question
[PDF] Deep Learning for Question Answering - UMass CICS
Deep Learning for Question Answering Mohit Iyyer Briefly: deep learning + NLP basics Answers can appear as part of question text (e g a
[PDF] Question paper
Question paper Please answer Part-A and Part-B in separate answer books used in all the deep learning approaches we talked about
What questions can be answered by machine learning?
Machine learning can be challenging, as it involves understanding complex mathematical concepts and algorithms, as well as the ability to work with large amounts of data. However, with the right resources and support, it is possible to learn and become proficient in machine learning.Is machine learning so hard?
The reinforcement learning is hardest part of machine learning. The most important results in deep learning such as image classification so far were obtained by supervised learning or unsupervised learning.What is hardest in machine learning?
How Do I Get Started?
1Step 1: Adjust Mindset. Believe you can practice and apply machine learning. 2Step 2: Pick a Process. Use a systemic process to work through problems. 3Step 3: Pick a Tool. Select a tool for your level and map it onto your process. 4Step 4: Practice on Datasets. 5Step 5: Build a Portfolio.
10-601 Machine Learning, Midterm Exam
Instructors: Tom Mitchell, Ziv Bar-Joseph
Monday 22
ndOctober, 2012There are 5 questions, for a total of 100 points.
This exam has 16 pages, make sure you have all pages before you begin. This exam is open book, open notes, butno computers or other electronic devices.Good luck!
Name:Andrew ID:
QuestionPointsScore
Short Answers20
Comparison of ML algorithms20
Regression20
Bayes Net20
Overfitting and PAC Learning20
Total:100
110-601 Machine Learning Midterm Exam October 18, 2012
Question 1.Short Answers
True False Questions.
(a) [1 point] W ecan get multiple local optimum solutions if we solve a linear r egressionpr oblemby minimizing the sum of squared errors using gradient descent.True False
Solution:
False(b)[1 point] When a decision tr eeis gr ownto full depth, it is mor elikely to fit the noise in the data.
True False
Solution:
True(c)[1 point] When the hypothesis space is richer ,over fitting is mor elikely .True False
Solution:
True(d)[1 point] When the featur espace is lar ger,over fitting is mor elikely .True False
Solution:
True(e)[1 point] W ecan use gradient descent to learn a Gaussian Mixtur eModel.True False
Solution:
TrueShort Questions.
(f) [3 points] Can you r epresentthe following boolean function with a single logistic thr esholdunit(i.e., a single unit from a neural network)? If yes, show the weights. If not, explain why not in 1-2
sentences.A B f(A,B) 1 1 0 0 0 0 1 0 1 0 1 0Page 1 of 16
10-601 Machine Learning Midterm Exam October 18, 2012
Solution:
Yes, you can represent this function with a single logistic threshold unit, since it is linearly separable. Here is one example.F(A;B) = 1fAB0:5>0g(1)
Page 2 of 16
10-601 Machine Learning Midterm Exam October 18, 2012
(g) [3 points] Suppose we cluster eda set of N data points using two dif ferentclustering algorithms: k-means and Gaussian mixtures. In both cases we obtained 5 clusters and in both cases the centers of the clusters are exactly the same. Can 3 points that are assigned to different clusters in the k- means solution be assigned to the same cluster in the Gaussian mixture solution? If no, explain. If so, sketch an example or explain in 1-2 sentences.Solution:
Yes, k-means assigns each data point to a unique cluster based on its distance to the cluster center. Gaussian mixture clustering gives soft (probabilistic) assignment to each data point. Therefore, even if cluster centers are identical in both methods, if Gaussian mixture compo- nents have large variances (components are spread around their center), points on the edgesbetween clusters may be given different assignments in the Gaussian mixture solution.Circle the correct answer(s).
(h) [3 points] As the number of training examples goes to infinity ,your model trained on that data will have: A. Lower variance B. Higher variance C. Same varianceSolution:
Lower variance(i)[3 points] As the number of training examples goes to infinity ,your model trained on that data
will have:A. Lower bias B. Higher bias C. Same bias
Solution:
Same bias(j)[3 points] Suppose you ar egiven an EM algorithm that finds maximum likelihood estimates for a
model with latent variables. You are asked to modify the algorithm so that it finds MAP estimates instead. Which step or steps do you need to modify: A. Expectation B. Maximization C. No modification necessary D. BothSolution:
MaximizationPage 3 of 16
10-601 Machine Learning Midterm Exam October 18, 2012
Question 2.Comparison of ML algorithms
Assume we have a set of data from patients who have visited UPMC hospital during the year 2011. Aset of features (e.g., temperature, height) have been also extracted for each patient. Our goal is to decide
whether a new visiting patient has any of diabetes, heart disease, or Alzheimer (a patient can have one
or more of these diseases). (a) [3 points] W ehave decided to use a neural network to solve this pr oblem.W ehave two choices: either to train aseparateneural network for each of the diseases or to train a single neural network with one output neuron for each disease, but with a shared hidden layer. Which method do you prefer? Justify your answer.Solution:
1- Neural network with a shared hidden layer can capture dependencies between diseases.
It can be shown that in some cases, when there is a dependency between the output nodes, having a shared node in the hidden layer can improve the accuracy.2- If there is no dependency between diseases (output neurons), then we would prefer to have
a separate neural network for each disease.(b)[3 points] Some patient featur esar eexpensive to collect (e.g., brain scans) wher easothers ar enot
quotesdbs_dbs2.pdfusesText_2[PDF] machine learning research papers 2019 ieee
[PDF] machine learning research papers 2019 pdf
[PDF] machine learning solved question paper
[PDF] machine learning tutorial pdf
[PDF] machine learning with python ppt
[PDF] macintosh
[PDF] macleay valley travel reviews
[PDF] macleay valley travel tasmania
[PDF] macos 10.15 compatibility
[PDF] macos catalina security features
[PDF] macos security guide
[PDF] macos server
[PDF] macos server mojave
[PDF] macos virtualization license
