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Questions and Answers
What is a defining characteristic of a microkernel operating system?
What is a defining characteristic of a microkernel operating system?
Which scheduling algorithm is characterized as pre-emptive?
Which scheduling algorithm is characterized as pre-emptive?
In process management, what is a Process Control Block (PCB) primarily used for?
In process management, what is a Process Control Block (PCB) primarily used for?
Which of the following is not a criterion for scheduling algorithms?
Which of the following is not a criterion for scheduling algorithms?
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Which concept refers to a situation where multiple processes access a shared resource and manipulate it simultaneously, leading to data inconsistency?
Which concept refers to a situation where multiple processes access a shared resource and manipulate it simultaneously, leading to data inconsistency?
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What is the primary function of semaphores in inter-process communication?
What is the primary function of semaphores in inter-process communication?
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Which operating system structure is most rigid and lacks modularity?
Which operating system structure is most rigid and lacks modularity?
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In context switching, what happens to the current process?
In context switching, what happens to the current process?
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Which of the following correctly describes a zombie process?
Which of the following correctly describes a zombie process?
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Which semaphore functions are commonly used for signaling between processes?
Which semaphore functions are commonly used for signaling between processes?
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What is the purpose of the pthread_join function in POSIX Threads?
What is the purpose of the pthread_join function in POSIX Threads?
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Which of the following describes message passing in inter-process communication?
Which of the following describes message passing in inter-process communication?
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In object-oriented programming, what defines encapsulation?
In object-oriented programming, what defines encapsulation?
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Which programming function is used to create a pipe for inter-process communication?
Which programming function is used to create a pipe for inter-process communication?
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What key feature distinguishes polymorphism in object-oriented programming?
What key feature distinguishes polymorphism in object-oriented programming?
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What is the primary purpose of implementing Bagging using Random Forests?
What is the primary purpose of implementing Bagging using Random Forests?
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Which of the following statements is NOT a feature of object-oriented programming?
Which of the following statements is NOT a feature of object-oriented programming?
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K-means clustering is best used for which of the following tasks?
K-means clustering is best used for which of the following tasks?
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Which statement accurately describes Gaussian Mixture Model (GMM)?
Which statement accurately describes Gaussian Mixture Model (GMM)?
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What is the main advantage of using Association Rule Mining with FP Growth?
What is the main advantage of using Association Rule Mining with FP Growth?
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When comparing machine learning algorithms, which factor is crucial?
When comparing machine learning algorithms, which factor is crucial?
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What is the role of the k-nearest neighbor algorithm in machine learning?
What is the role of the k-nearest neighbor algorithm in machine learning?
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Which clustering method is best suited for categorical data?
Which clustering method is best suited for categorical data?
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What is a key characteristic of evaluating ML algorithms with unbalanced datasets?
What is a key characteristic of evaluating ML algorithms with unbalanced datasets?
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What are the six stages of the lifecycle management of cloud services?
What are the six stages of the lifecycle management of cloud services?
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Which aspect is NOT a concern related to cloud security?
Which aspect is NOT a concern related to cloud security?
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What does Service Oriented Architecture (SOA) primarily utilize for transactions?
What does Service Oriented Architecture (SOA) primarily utilize for transactions?
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Which of the following options correctly describes cloud storage?
Which of the following options correctly describes cloud storage?
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What is one of the key features of cloud management systems?
What is one of the key features of cloud management systems?
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Which protocol stack is involved in an Event-driven Service Oriented Architecture?
Which protocol stack is involved in an Event-driven Service Oriented Architecture?
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Which one of the following is a characteristic of cloud APIs?
Which one of the following is a characteristic of cloud APIs?
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What is the function of syndication services in webmail?
What is the function of syndication services in webmail?
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What is a primary focus of non-metric methods in pattern classification?
What is a primary focus of non-metric methods in pattern classification?
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Which of the following is NOT an algorithm used for clustering?
Which of the following is NOT an algorithm used for clustering?
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What does unsupervised learning primarily involve?
What does unsupervised learning primarily involve?
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What is the objective of learning about vertex coloring in graph theory?
What is the objective of learning about vertex coloring in graph theory?
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Which of the following concepts is NOT covered under the basics of graph theory?
Which of the following concepts is NOT covered under the basics of graph theory?
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In the context of clustering, what do criterion functions do?
In the context of clustering, what do criterion functions do?
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What is the primary purpose of set covering in graph theory?
What is the primary purpose of set covering in graph theory?
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What is the relation between K-means clustering and the properties of the data?
What is the relation between K-means clustering and the properties of the data?
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What does the chromatic number of a graph represent?
What does the chromatic number of a graph represent?
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Which theorem pertains to the coloring of chordal graphs?
Which theorem pertains to the coloring of chordal graphs?
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In which type of graph is edge-coloring specifically relevant?
In which type of graph is edge-coloring specifically relevant?
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What characteristic distinguishes Class-1 graphs from Class-2 graphs?
What characteristic distinguishes Class-1 graphs from Class-2 graphs?
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What does the Greedy coloring algorithm aim to achieve?
What does the Greedy coloring algorithm aim to achieve?
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Which kind of networks does Maarten van Steen's book focus on?
Which kind of networks does Maarten van Steen's book focus on?
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What is the primary focus of the Gupta-Vizing theorem?
What is the primary focus of the Gupta-Vizing theorem?
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What can Mycielski's theorem help to construct?
What can Mycielski's theorem help to construct?
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Study Notes
Probability & Statistics (PCCAIML 501)
- Course Semester: V
- Maximum Marks: 100
-
Examination Scheme:
- End Semester Exam: 70 marks
- Attendance: 5 marks
- Continuous Assessment: 25 marks
- Teaching Scheme: 3 hours/week theory
- Course Aim: Equip students with standard concepts and tools in probability and statistics to tackle problems in the discipline.
-
Course Objective: Familiarize students with statistical techniques. Students should demonstrate understanding of probability & statistics by learning:
- Probability and random variables (discrete and continuous) and their properties
- Basic ideas of statistics (central tendency, correlation, regression)
- Statistical methods for studying data samples
Object Oriented Programming (PCC-CS503)
- Semester: V
- Maximum Marks: 100
-
Examination Scheme:
- Mid Semester Exam: 15 marks
- Assignment and Quiz: 10 marks
- Attendance: 5 marks
- End Semester Exam: 70 marks
Object Oriented Programming Lab (PCC-CS592)
- Semester: V
- Maximum Marks: 100
- Credit Points: 2
- Teaching Scheme: 4 hours/week
Introduction to Machine Learning (PCCAIML 502)
- Semester: V
- Maximum Marks: 100
- Credit Points: 3
- L: 3, T: 0, P: 0, C: 3
- Course Objectives: Students will comprehend supervised and unsupervised techniques, differentiate regression/classification/clustering, analyze the performance of various machine learning techniques and select appropriate features.
- Module 1: Introduction to Machine Learning, Feature Engineering, Learning Paradigm, Generalization of Hypothesis, VC Dimension, PAC Learning, Applications of ML.
- Module 2: Data Handling and ANN, Feature Selection Mechanisms, Imbalanced Data, Outlier Detection. Artificial Neural Network details including backpropagation.
- Module 3: ML Models and Evaluation, Regression (Multi-variable Regression, Model evaluation, Least squares regression, Regularization, LASSO), Classification (KNN, Naive Bayes, SVM, Decision Tree). Training and testing classifier models, Cross-validation, Model evaluation (precision, recall, F1-mesure, accuracy, area under curve), Statistical decision theory (discriminant functions, decision surfaces). Models assessment and inference.
- Module 4: Model Assessment and Inference,Model assessment and Selection Ensemble Learning (Boosting, Bagging). Bayesian Theory, EM Algorithm.
- Module 5: Hidden Markov Models
- Module 6: Association Rules
- Pre-requisites: NIL
Machine Learning Lab (PCCAIML 592)
- Semester: V
- Maximum Marks: 100
- Credit Points: 2
- Lab Experiments: Implement Decision Tree learning, Logistic Regression, Classification using Multilayer perceptron, Classification using SVM, Implement Adaboost, Implement Bagging(Random forests), Implement K-means Clustering, Implement Hierarchical Clustering, Implement K-mode clustering, Implement Association Rule Mining using FP Growth, Classification based on association rules, Implement Gaussian Mixture Model using the EM algorithm, Evaluating ML algorithm with balanced and unbalanced datasets, Comparison of machine learning algorithms. Implement K-nearest neighbour algorithms.
- Total Lecture hours: 30 hours
Graph Theory (PECAIML501C)
- Semester: V
- Credit Points: 3
- Teaching Scheme: 3 hours theory/week
- Aim: Understanding graph theory, paths, walks and cycles, set covering, matching problem, vertex coloring.
- Module details: 1. Introduction, 2. Connected Graphs & shortest paths, 3. Trees, 4. Independent sets & coverings, 5.Vertex Colouring.
Pattern Recognition (PECAIML501B)
- Semester: V
- Credit Points: 3
- Teaching scheme: 3 hours theory/week
- Aim: Understand Bayesian decision theory, classifiers, discriminant functions, normal/density/discriminant functions.
- Module details: basics of pattern recognition, Bayesian decision theory, parameter estimation methods, Hidden Markov Models, dimension reduction methods, non-parametric techniques, linear discriminant function based classifier (Perceptron, Support Vector Machines).
Introduction to Industrial Management (HSMC-501)
- Semester: V
- Credit Points: 3
- Teaching Scheme: 2 hours theory/week
- Course Objectives: Students should acquire knowledge of the basic principles of industrial management and organizational structure. Topics include definition, types and factors in organization structures, and the concept of division of labor.
- Module Details: Introduction to Industrial Management; Concepts; types, parameters, variables and behaviour; Management definitions; organizational structure, definitions, goals, factors considered in formulating structure; Types of organizations; Advantages/Disadvantages/Applications; Division of labour, span of control, delegation; Organizational culture and climate; Moral factors; Job satisfaction; Factory acts and labour la
Cloud Computing (PECAIML501A)
- Semester: V
- Credit Points: 3
- Teaching Scheme: 3 hrs./week theory
- Unit 1: Definition of Cloud computing and its basics (cloud types, deployment types, service models- IaaS, PaaS, SaaS). Cloud Reference Model, Infrastructure, Platforms, Virtual Appliances, Communication Protocols, Application development; Usage of PaaS; Application frameworks; Google applications portfolio.
- Unit 2: Use of platforms in Cloud computing (Mobility patterns, Load balancing, Application Delivery Controllers and Networks, Hypervisors); Porting of applications into the Cloud;Concepts of Platform as a Service, PaaS, and Distinction between SaaS and PaaS (examples of Salesforce.com and Force.com);
- Unit 3: Cloud Management: Overview of network management, Lifecycle Management of Cloud services; Concept of Security concerns, Security mapping, data security, storage, and identity management; Cloud Transactions; functionality mapping.
- Unit 4: Cloud infrastructure; Cloud Management; Concepts of services and applications-Service Oriented Architecture.
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Description
This quiz covers key concepts in probability and statistics as outlined in the PCCAIML 501 course. Students will explore topics including random variables, central tendency, correlation, and regression. Prepare to demonstrate your understanding through various statistical techniques.
