ANU UG/Degree 4th Sem(Y23) Machine Learning Using Python Unit Wise Important Questions

ANU UG/Degree 4th Sem(Y23)  Machine Learning Using Python Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

 

UNIT I INTRODUCTION TO MACHINE LEARNING

Review of Linear Algebra for machine learning; Introduction and motivation for machine learning; Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization, Bias variance trade-off. 

Short Answer Questions

1. What role does linear algebra play in machine learning, and why is it essential for model formulation and computation?
2. What are the main motivations behind developing and applying machine learning techniques?
3. Provide an example of a machine learning application and discuss its impact on a specific industry or field.
4. Define the Vapnik-Chervonenkis (VC) dimension and explain its significance in evaluating model complexity.
5. Explain the concept of Probably Approximately Correct (PAC) learning and its role in assessing learning algorithms.
6. Describe the concepts of hypothesis spaces and inductive bias, and explain how the bias-variance trade-off affects model generalization.

Long Answer Questions

1. Discuss the importance of linear algebra in machine learning, including its role in algorithms such as principal component analysis (PCA) and support vector machines (SVM).
2. Explain the key motivations behind machine learning, focusing on the need for automation, handling large datasets, and uncovering hidden patterns.
3. Explore various machine learning applications across different industries, and analyze how these applications transform decision-making and operational efficiency.
4. Provide a detailed explanation of the Vapnik-Chervonenkis (VC) dimension and Probably Approximately Correct (PAC) learning, discussing how these theoretical frameworks inform model complexity and reliability.
5. Define hypothesis spaces and inductive bias in the context of machine learning, and discuss how these factors influence the learning process and model generalization.
6. Elaborate on the bias-variance trade-off, explaining its impact on overfitting and underfitting, and discuss strategies for achieving optimal generalization in machine learning models.

UNIT II SUPERVISED LEARNING
Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function – Perceptron algorithm, Probabilistic discriminative model - Logistic regression, Probabilistic generative model – Naive Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random Forests

Short Answer Questions

1. Define the least squares method in linear regression and explain its primary objective.
2. Compare single-variable and multiple-variable linear regression models.
3. What is Bayesian linear regression and how does it differ from classical linear regression?
4. Describe the role of gradient descent in optimizing linear regression models.
5. Explain the perceptron algorithm and its use as a linear classification method.
6. Contrast logistic regression (a probabilistic discriminative model) with Naive Bayes (a probabilistic generative model) in terms of their classification approaches.

Long Answer Questions

1. Discuss the fundamental concepts of linear regression, including the least squares method, differences between single-variable and multiple-variable models, Bayesian linear regression, and the role of gradient descent in model optimization.
2. Compare and contrast linear classification techniques by explaining the discriminant function and the perceptron algorithm, including their strengths and limitations.
3. Analyze logistic regression as a probabilistic discriminative model, detailing its theoretical foundations, parameter estimation, and practical applications in classification.
4. Explain the principles of Naive Bayes as a probabilistic generative model, discussing its assumptions, implementation, and scenarios where it performs effectively.
5. Elaborate on the concept of maximum margin classifiers by discussing support vector machines, including how they determine decision boundaries and improve classification accuracy.
6. Evaluate decision trees and random forests as supervised learning methods, focusing on their methodologies, advantages, limitations, and comparative performance in various classification tasks.

UNIT III ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning - bagging, boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization.

Short Answer Questions

1. What is ensemble learning, and how does the voting scheme work as a model combination method?
2. Define bagging and explain its role in reducing variance in ensemble techniques.
3. How does boosting improve model performance in an ensemble learning framework?
4. What is stacking in ensemble learning, and how does it differ from bagging and boosting?
5. Describe the basic objective of K-means clustering in unsupervised learning.
6. How do Gaussian mixture models utilize the Expectation Maximization algorithm in clustering tasks?

Long Answer Questions

1. Discuss various model combination schemes used in ensemble learning—including voting, bagging, boosting, and stacking—and explain how each method enhances predictive performance.
2. Compare and contrast bagging and boosting, outlining their mechanisms, advantages, and potential drawbacks in ensemble methods.
3. Explain the concept of stacking as an ensemble learning technique, detailing its structure, benefits, and challenges when integrating multiple learners.
4. Provide a detailed explanation of the K-means clustering algorithm, including its step-by-step process, applications, and limitations in handling different data types.
5. Elaborate on the principles of Gaussian mixture models and the role of the Expectation Maximization algorithm in clustering. Include a discussion on how these methods address data heterogeneity.
6. Analyze the significance of instance-based learning methods, such as KNN, in the context of data analysis, and compare their use with model-based unsupervised techniques like Gaussian mixture models.

UNIT IV NEURAL NETWORKS
Multilayer perceptron, activation functions, network training – gradient descent optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep networks –Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch normalization, regularization, dropout

Short Answer Questions

1. What is a multilayer perceptron, and how does it differ from a single-layer perceptron?
2. Name two common activation functions and describe their roles in neural networks.
3. What is gradient descent optimization, and why is it critical in training neural networks?
4. How does stochastic gradient descent differ from standard gradient descent?
5. What is error backpropagation, and how does it facilitate weight updates in a neural network?

6. Define unit saturation (vanishing gradient problem) and explain its impact on deep network training.

Long Answer Questions

1. Discuss the architecture of a multilayer perceptron, including the role of activation functions such as ReLU, and explain how these components contribute to learning complex patterns.
2. Explain the process of network training in neural networks, focusing on gradient descent optimization and stochastic gradient descent, and describe how these methods help minimize the error function.
3. Elaborate on the error backpropagation algorithm, detailing its steps and how it propagates errors backward through the network to update weights effectively.
4. Analyze the vanishing gradient problem (unit saturation) in deep neural networks, discussing its causes, the challenges it presents, and strategies to overcome it.
5. Compare and contrast various activation functions (e.g., sigmoid, tanh, ReLU), discussing their mathematical properties and the implications for network performance and convergence.
6. Discuss the importance of hyperparameter tuning, batch normalization, regularization, and dropout in neural network training, explaining how each technique improves generalization and mitigates overfitting.

UNIT V DESIGN AND ANALYSIS OF MACHINE LEARNING EXPERIMENTS
Guidelines for machine learning experiments, Cross Validation (CV) and resampling – K-fold CV, bootstrapping, measuring classifier performance, assessing a single classification algorithm and comparing two classification algorithms – t test, McNemar’s test, K-fold CV paired t test

Short Answer Questions

1. What are the key guidelines for designing machine learning experiments, and why are they important for reproducibility and validity?
2. Define cross-validation and explain its role in assessing model performance.
3. What is K-fold cross-validation, and what are its main advantages over other resampling methods?
4. How does bootstrapping differ from K-fold cross-validation in evaluating a classifier's performance?
5. What are some common metrics used for measuring classifier performance, and why is it important to use multiple metrics?
6. Explain the purpose of statistical tests such as the t-test and McNemar’s test in comparing classification algorithms.

Long Answer Questions

1. Discuss the key guidelines for designing and analyzing machine learning experiments, including considerations for data splitting, parameter tuning, and ensuring experimental reproducibility.
2. Explain the concept of cross-validation in detail, comparing K-fold cross-validation and bootstrapping. Highlight the advantages and limitations of each method.
3. Describe various classifier performance metrics (such as accuracy, precision, recall, F1 score, and ROC-AUC). How do these metrics contribute to a comprehensive evaluation of a model?
4. Elaborate on the process of assessing the performance of a single classification algorithm using cross-validation and resampling techniques. What are the potential pitfalls and how can they be mitigated?
5. Compare and contrast the use of the t-test, McNemar’s test, and the K-fold CV paired t-test in evaluating and comparing the performance of two classification algorithms. Discuss their assumptions and applicability.
6. Analyze the importance of resampling methods in machine learning experiments. How do techniques like cross-validation and bootstrapping improve the reliability and generalizability of model performance estimates?

ANU UG/Degree 4th Sem(Y23) Data Warehousing and Data Mining Unit Wise Important Questions

 ANU UG/Degree 4th Sem(Y23) Data Warehousing and Data Mining Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

UNIT I

Data Warehousing and Business Analysis: - Data warehousing Components –Building a Data warehouse –Data Warehouse Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools –Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis.

Short Answer Questions

1. Define data warehousing and list its key components.
2. What are the essential steps involved in building a data warehouse?
3. Describe the typical architecture of a data warehouse.
4. What are DBMS schemas for decision support, and how do they differ from operational schemas?
5. Explain the role of data extraction, cleanup, and transformation tools in the data warehousing process.
6. What is Online Analytical Processing (OLAP) and how does it facilitate multidimensional data analysis?

Long Answer Questions

1. Discuss in detail the various components of a data warehouse, explaining how each contributes to effective business analysis.
2. Outline the process of building a data warehouse from planning through implementation, and analyze the challenges encountered during each phase.
3. Compare different data warehouse architectures (e.g., centralized, federated, data marts) and evaluate their advantages and disadvantages in a business context.
4. Explain the concept of DBMS schemas for decision support systems, detailing common schema models such as star schema and snowflake schema, and their impact on query performance.
5. Analyze the importance of data extraction, cleanup, and transformation (ETL) tools in ensuring data quality within a data warehouse, and discuss best practices for ETL processes.
6. Elaborate on the functionalities of Online Analytical Processing (OLAP), describing its role in multidimensional data analysis and reporting, and provide examples of how OLAP tools support business decision-making.

UNIT II

Data Mining: - Data Mining Functionalities – Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept Hierarchy Generation- Architecture Of A Typical Data Mining Systems- Classification Of Data Mining Systems.

Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – Constraint-Based Association Mining.

Short Answer Questions

1. What are the key functionalities of data mining?
2. What is data preprocessing, and how do data cleaning, integration, and transformation contribute to this process?
3. How does data reduction enhance the efficiency of data mining operations?
4. Explain data discretization and the process of concept hierarchy generation in data mining.
5. Describe the architecture of a typical data mining system and identify its main components.
6. How are data mining systems classified based on their functionalities and architecture?

Long Answer Questions

1. Discuss in detail the key functionalities of data mining and provide examples of how these functionalities are applied in real-world scenarios.
2. Explain the data preprocessing phase in data mining by covering data cleaning, data integration, and data transformation. What challenges and best practices are involved?
3. Analyze the role of data reduction in data mining. Describe various techniques used and their impact on processing efficiency.
4. Elaborate on the methods of data discretization and concept hierarchy generation, discussing their benefits and potential limitations.
5. Describe the architecture of a typical data mining system, including a detailed explanation of its main components and their interactions.
6. Compare and contrast different classifications of data mining systems, discussing how data types, mining techniques, and application domains influence their categorization.

UNIT III
Classification and Prediction: - Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Backpropagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section. 

Short Answer Questions

1. What are the primary challenges and issues associated with classification and prediction in data mining?
2. Explain the basic working principle of decision tree classification.
3. How does Bayesian classification operate, and what key assumption underpins its methodology?
4. Define rule-based classification and describe one real-world application.
5. What are Support Vector Machines (SVMs), and why are they effective in handling complex classification tasks?
6. Briefly explain the concept of ensemble methods and their role in improving the accuracy of classifiers.

Long Answer Questions

1. Discuss the various issues related to classification and prediction, such as overfitting, underfitting, and high-dimensionality. Provide strategies to address these challenges.
2. Compare and contrast different classification techniques—including decision trees, Bayesian methods, rule-based systems, and backpropagation-based classification—highlighting their strengths, weaknesses, and suitable application scenarios.
3. Elaborate on Support Vector Machines (SVMs) and associative classification methods. Describe how these techniques work and discuss the advantages and limitations of each.
4. Define lazy learners in the context of classification. Explain how they differ from eager learners and provide examples of when lazy learning might be preferred.
5. Explain the concept of prediction in data mining, detailing various accuracy and error measures used to evaluate classifiers or predictors. Discuss how these metrics influence model selection.
6. Analyze ensemble methods and model selection in classification and prediction. Describe common ensemble techniques, such as bagging and boosting, and explain how they contribute to improved predictive performance.

 UNIT IV
Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods – Model-Based Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis.

Short Answer Questions

1. What types of data are typically used in cluster analysis, and how do they influence the clustering process?
2. Define partitioning methods in cluster analysis and provide one example.
3. How does hierarchical clustering differ from partitioning methods?
4. What is the main principle behind density-based clustering methods?
5. Describe grid-based clustering and mention one key advantage it offers when handling large datasets.
6. What is outlier analysis in cluster analysis, and why is it important?

Long Answer Questions

1. Discuss the various types of data used in cluster analysis and explain how the characteristics of these data types can affect the choice of clustering method.
2. Provide a detailed comparison of the major clustering methods—partitioning, hierarchical, density-based, grid-based, and model-based. Include their key features, advantages, and limitations.
3. Explain partitioning methods in depth. Describe common algorithms, how they work, and discuss scenarios where partitioning methods are most effective.
4. Compare and contrast hierarchical clustering with density-based clustering. Highlight the underlying mechanisms, the types of clusters each method tends to identify, and their applicability in different contexts.
5. Describe grid-based and model-based clustering methods. Discuss their methodologies and evaluate their effectiveness in processing high-dimensional and large-scale datasets.
6. Elaborate on constraint-based cluster analysis and the role of outlier analysis. How do constraints improve clustering outcomes, and what techniques are used to identify and manage outliers in clusters?

UNIT V

Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.

Short Answer Questions

1. What is meant by mining complex data objects and how does multidimensional analysis support descriptive mining?
2. How does spatial data mining differ from traditional data mining methods?
3. What are some key challenges unique to multimedia data mining?
4. Define text mining and mention one common technique used in extracting useful information from textual data.
5. What distinguishes web mining from other forms of data mining?
6. Explain the concept of descriptive mining and its significance in analyzing complex data objects.

Long Answer Questions

1. Discuss in detail the process of multidimensional analysis and descriptive mining of complex data objects. How do these techniques differ from standard data mining approaches, and what advantages do they offer?
2. Explain the methodologies and challenges associated with spatial data mining. Provide examples of spatial data and discuss its practical applications in various industries.
3. Elaborate on multimedia data mining by describing the techniques used to process and analyze multimedia content, as well as the challenges involved in handling diverse data types such as images, audio, and video.
4. Provide a comprehensive overview of text mining, including key preprocessing steps, common algorithms, and real-world applications in areas such as sentiment analysis and information retrieval.
5. Compare and contrast web mining with traditional data mining techniques. What unique challenges does mining the World Wide Web present, and how are these challenges typically addressed?
6. Evaluate how integrating mining of object, spatial, multimedia, text, and web data can enhance modern business analytics. Discuss how these diverse mining techniques complement each other to provide a more comprehensive view of data.

ANU UG/Degree 4th Sem(Y23) Auditing Unit Wise Important Questions

ANU UG/Degree 4th Sem(Y23) Database Management System with Oracle Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

Unit 1: Introduction: Meaning – Objectives – Importance of Auditing – Characteristics - Book Keeping vs Auditing - Accounting vs Auditing – Role of Auditor in Checking Corporate Frauds.

Short Answer Questions

1. Define auditing and explain its primary objectives.
2. What is the importance of auditing in ensuring transparency and accountability in organizations?
3. List and describe two key characteristics of auditing.
4. Differentiate between book keeping and auditing.
5. Compare accounting and auditing in terms of their scope, purpose, and methodology.
6. Describe the role of an auditor in detecting and preventing corporate fraud.

Long Answer Questions

1. Discuss the meaning and objectives of auditing, and explain its importance in the context of modern business practices.
2. Analyze the key characteristics of auditing and how they contribute to ensuring accuracy and reliability in financial reporting.
3. Compare and contrast book keeping with auditing, highlighting the distinct roles and processes involved in each.
4. Examine the differences between accounting and auditing, and discuss how each function supports overall financial management.
5. Evaluate the significance of auditing in enhancing corporate governance and ensuring stakeholder confidence.
6. Critically assess the role of auditors in checking corporate frauds, including the challenges they face and the strategies they employ to detect fraudulent activities.

 Unit 2: Types of Audit: Based on Ownership, Time and Objective - Independent, Financial, Internal, Cost, Tax, Government, Secretarial Audits

Short Answer Questions

1. Define an independent audit and explain its main objective.
2. What is a financial audit, and how does it differ from a cost audit?
3. Describe the primary purpose of an internal audit.
4. What is a tax audit, and which aspect of an organization does it primarily focus on?
5. Outline the key features of a government audit.
6. What is a secretarial audit and how is it distinct from other audit types?

Long Answer Questions

1. Discuss the various types of audits based on ownership, time, and objectives, highlighting the unique characteristics of independent, financial, and internal audits.
2. Compare and contrast financial audits and cost audits, explaining their scopes, methodologies, and roles in ensuring organizational accountability.
3. Explain the significance of internal audits within an organization. Include a discussion on how they contribute to operational efficiency and risk management.
4. Analyze the purpose and process of tax audits, detailing how they ensure compliance with tax regulations and their impact on financial reporting.
5. Examine the role of government audits in the public sector, discussing their objectives, challenges, and the importance of accountability in governmental operations.
6. Evaluate the concept of secretarial audits, including their regulatory framework and their contribution to good corporate governance and transparency.

 Unit 3: Planning of Audit: Steps to be taken at the Commencement of a New Audit – Audit Programme - Audit Note Book– Audit Working Papers - Audit Evidence - Internal Check, Internal Audit and Internal Control.

Short Answer Questions

1. What are the key steps to be taken at the commencement of a new audit during the planning phase?
2. Define an audit programme and briefly describe its main components.
3. What is an Audit Note Book, and what role does it play in the audit process?
4. Explain the purpose of audit working papers and why they are essential for auditors.
5. Define audit evidence and mention two types of evidence commonly gathered during an audit.
6. Differentiate between internal check, internal audit, and internal control.

Long Answer Questions

1. Discuss in detail the steps involved at the commencement of a new audit. Explain why careful planning is crucial for the overall success of the audit process.
2. Elaborate on the concept of an audit programme. What are its key components, and how does it guide auditors in conducting a systematic and thorough audit?
3. Explain the roles and differences between an Audit Note Book and audit working papers. How do these documents contribute to effective audit documentation and evidence collection?
4. Define audit evidence and discuss the various types of evidence that auditors collect. How do auditors evaluate the reliability and sufficiency of this evidence during the audit process?
5. Examine the roles of internal check, internal audit, and internal control within an organization. How do these mechanisms support the audit process and contribute to maintaining organizational integrity?
6. Analyze how the planning tools—such as the audit programme, note book, and working papers—integrate with overall audit execution and follow-up. Discuss the challenges auditors might face in ensuring effective internal controls and documentation.

 Unit 4: Vouching and Investigation: Definition and Importance of Vouching – Objectives of Vouching -Vouching of Cash and Trading Transactions – Investigation - Auditing vs. Investigation

Short Answer Questions

1. Define vouching and explain its importance in the auditing process.
2. What are the primary objectives of vouching in an audit?
3. How is vouching for cash transactions performed, and why is it critical?
4. Describe the process and significance of vouching for trading transactions.
5. Define investigation in the context of auditing and outline its purpose.
6. Differentiate between auditing and investigation in terms of scope and approach.

Long Answer Questions

1. Discuss in detail the definition and importance of vouching in auditing, highlighting how it contributes to the verification of financial records.
2. Analyze the objectives of vouching and explain how these objectives help ensure the accuracy and reliability of an organization's financial statements.
3. Describe the procedures involved in vouching for cash transactions, including key steps and potential challenges an auditor may encounter.
4. Elaborate on the vouching process for trading transactions and discuss its role in validating the authenticity of sales and purchase records.
5. Explain the concept of investigation within auditing, outlining its methodologies, scope, and the circumstances under which an investigation is initiated.
6. Compare and contrast auditing and investigation by discussing their respective purposes, processes, and how they complement each other in maintaining financial integrity.

 Unit 5: Company Audit and Auditors Report: Auditor's Qualifications – Appointment and Reappointment – Rights, Duties, Liabilities and Disqualifications - Audit Report: Contents –Preparation - Relevant Provisions of Companies Act, 2013.

Short Answer Questions

1. What are the key qualifications required for an auditor under the Companies Act, 2013?
2. Outline the process of appointment and reappointment of auditors in a company.
3. What rights are granted to auditors during the audit process as per regulatory provisions?
4. List two primary duties of an auditor that ensure effective audit execution.
5. What are some common liabilities and disqualifications applicable to auditors under the Companies Act, 2013?
6. Identify the essential contents that must be included in an audit report according to the Companies Act, 2013.

Long Answer Questions

1. Explain in detail the qualifications required for an auditor as per the Companies Act, 2013. Discuss how these qualifications enhance the reliability and credibility of the audit process.
2. Describe the entire process of appointment and reappointment of auditors in a company. Include the statutory provisions, criteria for independence, and how these procedures safeguard against conflicts of interest.
3. Discuss the rights and duties of auditors in the context of a company audit. Explain how these rights and responsibilities contribute to transparent and effective auditing while protecting stakeholders' interests.
4. Evaluate the liabilities and disqualifications imposed on auditors under the Companies Act, 2013. Provide examples and discuss the rationale behind these measures in promoting audit quality and corporate accountability.
5. Outline the key components that should be included in an audit report as mandated by the Companies Act, 2013. Describe the preparation process of an audit report and how it ensures comprehensive disclosure of audit findings.
6. Analyze the impact of the Companies Act, 2013 on the role and responsibilities of auditors. Discuss how the Act has influenced audit practices, auditor independence, and overall corporate governance.