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    Machine Learning QB

    Uploaded by

    Jyotsna Suraydevara
    This document outlines the topics covered in 5 units on machine learning. Unit 1 covers probability rules, Bayes' theorem, probability distributions including Poisson and binomial, linear algebra functions, vectors, matrices, and convex optimization. Unit 2 discusses regression analysis, linear regression, regularization techniques, PCA, and PLS. Unit 3 focuses on linear classification, logistic regression, LDA, QDA, perceptron, SVM, backpropagation, ID3, and Naive Bayes. Unit 4 examines hypothesis testing, ensemble methods, random forests, bagging, boosting, clustering methods, hierarchical clustering, K-means, and DBSCAN. Finally, Unit 5 presents EM algorithm, Gaussian mixture models, machine learning architecture, learning models, version spaces, reinforcement

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    Machine Learning QB

    Uploaded by

    Jyotsna Suraydevara
    130 views3 pages
    This document outlines the topics covered in 5 units on machine learning. Unit 1 covers probability rules, Bayes' theorem, probability distributions including Poisson and binomial, linear algebra functions, vectors, matrices, and convex optimization. Unit 2 discusses regression analysis, linear regression, regularization techniques, PCA, and PLS. Unit 3 focuses on linear classification, logistic regression, LDA, QDA, perceptron, SVM, backpropagation, ID3, and Naive Bayes. Unit 4 examines hypothesis testing, ensemble methods, random forests, bagging, boosting, clustering methods, hierarchical clustering, K-means, and DBSCAN. Finally, Unit 5 presents EM algorithm, Gaussian mixture models, machine learning architecture, learning models, version spaces, reinforcement

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    This document outlines the topics covered in 5 units on machine learning. Unit 1 covers probability rules, Bayes' theorem, probability distributions including Poisson and binomial, linear algebra functions, vectors, matrices, and convex optimization. Unit 2 discusses regression analysis, linear regression, regularization techniques, PCA, and PLS. Unit 3 focuses on linear classification, logistic regression, LDA, QDA, perceptron, SVM, backpropagation, ID3, and Naive Bayes. Unit 4 examines hypothesis testing, ensemble methods, random forests, bagging, boosting, clustering methods, hierarchical clustering, K-means, and DBSCAN. Finally, Unit 5 presents EM algorithm, Gaussian mixture models, machine learning architecture, learning models, version spaces, reinforcement

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    130 views3 pages

    Machine Learning QB

    Uploaded by

    Jyotsna Suraydevara
    This document outlines the topics covered in 5 units on machine learning. Unit 1 covers probability rules, Bayes' theorem, probability distributions including Poisson and binomial, linear algebra functions, vectors, matrices, and convex optimization. Unit 2 discusses regression analysis, linear regression, regularization techniques, PCA, and PLS. Unit 3 focuses on linear classification, logistic regression, LDA, QDA, perceptron, SVM, backpropagation, ID3, and Naive Bayes. Unit 4 examines hypothesis testing, ensemble methods, random forests, bagging, boosting, clustering methods, hierarchical clustering, K-means, and DBSCAN. Finally, Unit 5 presents EM algorithm, Gaussian mixture models, machine learning architecture, learning models, version spaces, reinforcement

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    © All Rights Reserved
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    Download as docx, pdf, or txt
    of 3

    Machine Learning QB

    Unit 1

    1. Explain the probability rules with example problems. What are Independent Events
    in Probability? Explain, How to calculate the probability of independent events?

    2. Define and prove Baye’s theorem of probability


    3. what is the use of probability distribution? Explain its categories. What is Poisson
    Probability Distribution? Explain
    4. Write about Binomial Distribution with an example
    5. What is Linear Algebra ? How many ways we can define the linear algebra
    functions. Explain some linear algebra functions with examples
    6.explain about vectors and implementation of vectors with the help of some
    examples.
    7. what are the different forms of matrices? write about various operations performed
    on matrices
    8. what is convex optimisation ? Explain how can we solve convex optimization
    problem.

    9. What is statistical decision theory? Explain its framework


    10. Explain Bayesian Learning in terms of ML, MAP, Bayes Estimates and Conjugate
    Priors

    Unit 2
    1. what is Regression Analysis? What are the various types of regressions used in
    regression analysis

    2. Explain about Linear Regression model


    3. Elaborate Simple and Multiple Linear Regression Models
    4. What is Regularization? How does Regularization Work?

    5. what are the various techniques used for regularization ? write about them

    6. Write about Ridge and Lasso Regressions

    7. Write about Dimensionality Reduction Technique.

    8. what is PCA? Explain PCA algorithm

    9. How does principal component analysis help in dimension reduction?

    10. How PLS techniques is used in linear decomposition? Expalin


    Unit 3
    1. write about linear classification and its types
    2. Write about Logistic Regression.

    3. How can we use LDA technique for dimensionally Reduction

    4. write about Quardratic Discriminant analysis


    5. What is Perceptron? Explain about it.

    6. Explain Support Vector Machine Algorithm

    7. What is Backpropagation? Explain about the working of backpropagation with


    example.

    8. Explain ID3 algorithm with example

    9. what is the use of Bayes optimal classifier in the machine learning?

    10. Why is it called Naïve Bayes? Explain Naïve Bayes Algorithm with example

    Unit 4
    1. what is hypothesis testing? Explain about it with help of example
    2. write about different kinds of ensemble methods used in machine learning for
    predicting the data.
    3. Explain different kinds of voting techniques used for prediction

    4. Explain , how random forest algorithm works.


    5. what is Bagging technique? Why it is used for training data sets in ML
    6. write about the importance of AdaBoosting and Gradient Boosting ? Explain

    7. what is Clustering ? Write about various types of clustering Methods and


    algorithms .

    8. Explain Hierarchical Clustering in Machine Learning

    9.What is K-Means Algorithm? How does the K-Means Algorithm Work?

    10. Explain DBSCAN Algorithm with example

    Unit 5
    1. What is known as Expectation minimization. Explain EM algorithm. Explain, How
    can we present EM algorithm using probabilistic model
    2. Write about Gaussian mixture models  Explain EM algorithms for Gaussian Mixture
    Models
    3. What Is Machine Learning? What are the various components in machine learning
    architecture
    4. Explain various learning models in machine learning
    5. How to design the learning model ? Explain
    6. What are Version spaces ? How can we use version spaces in representation of
    knowledge. Explain with example

    7. Give the brief introduction of Reinforcement Learning and working of


    Reinforcement Learning

    8. What is the use of Bellman Equation in machine learning? Explain with example

    9. Explain Q-Learning algorithm of Reinforcement Learning Algorithms

    10. What are Bayesian Networks? How can we use Bayesian Networks in probability
    computations?. Explain with an example

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