Podcast
Questions and Answers
What is a defining characteristic of a microkernel operating system?
What is a defining characteristic of a microkernel operating system?
- It only supports one application at a time.
- It includes most services in the kernel space.
- It is designed for real-time processing exclusively.
- It minimizes the functions in the kernel space. (correct)
Which scheduling algorithm is characterized as pre-emptive?
Which scheduling algorithm is characterized as pre-emptive?
- Shortest Remaining Time First (SRTF) (correct)
- Shortest Job First (SJF)
- Round Robin (RR) (correct)
- First-Come, First-Served (FCFS)
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?
- To track memory allocation.
- To store data permanently.
- To manage the CPU state.
- To maintain information about processes. (correct)
Which of the following is not a criterion for scheduling algorithms?
Which of the following is not a criterion for scheduling algorithms?
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?
What is the primary function of semaphores in inter-process communication?
What is the primary function of semaphores in inter-process communication?
Which operating system structure is most rigid and lacks modularity?
Which operating system structure is most rigid and lacks modularity?
In context switching, what happens to the current process?
In context switching, what happens to the current process?
Which of the following correctly describes a zombie process?
Which of the following correctly describes a zombie process?
Which semaphore functions are commonly used for signaling between processes?
Which semaphore functions are commonly used for signaling between processes?
What is the purpose of the pthread_join function in POSIX Threads?
What is the purpose of the pthread_join function in POSIX Threads?
Which of the following describes message passing in inter-process communication?
Which of the following describes message passing in inter-process communication?
In object-oriented programming, what defines encapsulation?
In object-oriented programming, what defines encapsulation?
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?
What key feature distinguishes polymorphism in object-oriented programming?
What key feature distinguishes polymorphism in object-oriented programming?
What is the primary purpose of implementing Bagging using Random Forests?
What is the primary purpose of implementing Bagging using Random Forests?
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?
K-means clustering is best used for which of the following tasks?
K-means clustering is best used for which of the following tasks?
Which statement accurately describes Gaussian Mixture Model (GMM)?
Which statement accurately describes Gaussian Mixture Model (GMM)?
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?
When comparing machine learning algorithms, which factor is crucial?
When comparing machine learning algorithms, which factor is crucial?
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?
Which clustering method is best suited for categorical data?
Which clustering method is best suited for categorical data?
What is a key characteristic of evaluating ML algorithms with unbalanced datasets?
What is a key characteristic of evaluating ML algorithms with unbalanced datasets?
What are the six stages of the lifecycle management of cloud services?
What are the six stages of the lifecycle management of cloud services?
Which aspect is NOT a concern related to cloud security?
Which aspect is NOT a concern related to cloud security?
What does Service Oriented Architecture (SOA) primarily utilize for transactions?
What does Service Oriented Architecture (SOA) primarily utilize for transactions?
Which of the following options correctly describes cloud storage?
Which of the following options correctly describes cloud storage?
What is one of the key features of cloud management systems?
What is one of the key features of cloud management systems?
Which protocol stack is involved in an Event-driven Service Oriented Architecture?
Which protocol stack is involved in an Event-driven Service Oriented Architecture?
Which one of the following is a characteristic of cloud APIs?
Which one of the following is a characteristic of cloud APIs?
What is the function of syndication services in webmail?
What is the function of syndication services in webmail?
What is a primary focus of non-metric methods in pattern classification?
What is a primary focus of non-metric methods in pattern classification?
Which of the following is NOT an algorithm used for clustering?
Which of the following is NOT an algorithm used for clustering?
What does unsupervised learning primarily involve?
What does unsupervised learning primarily involve?
What is the objective of learning about vertex coloring in graph theory?
What is the objective of learning about vertex coloring in graph theory?
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?
In the context of clustering, what do criterion functions do?
In the context of clustering, what do criterion functions do?
What is the primary purpose of set covering in graph theory?
What is the primary purpose of set covering in graph theory?
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?
What does the chromatic number of a graph represent?
What does the chromatic number of a graph represent?
Which theorem pertains to the coloring of chordal graphs?
Which theorem pertains to the coloring of chordal graphs?
In which type of graph is edge-coloring specifically relevant?
In which type of graph is edge-coloring specifically relevant?
What characteristic distinguishes Class-1 graphs from Class-2 graphs?
What characteristic distinguishes Class-1 graphs from Class-2 graphs?
What does the Greedy coloring algorithm aim to achieve?
What does the Greedy coloring algorithm aim to achieve?
Which kind of networks does Maarten van Steen's book focus on?
Which kind of networks does Maarten van Steen's book focus on?
What is the primary focus of the Gupta-Vizing theorem?
What is the primary focus of the Gupta-Vizing theorem?
What can Mycielski's theorem help to construct?
What can Mycielski's theorem help to construct?
Flashcards
System Calls
System Calls
The basic building blocks of an operating system, these routines provide access to system resources like memory, files, and peripherals.
Context switching
Context switching
A way to run multiple programs or tasks concurrently on a single processor. It gives the illusion of multitasking by quickly switching between different processes.
Mutual Exclusion
Mutual Exclusion
A fundamental principle of operating systems that prevents multiple processes from interfering with each other's critical data. It ensures that shared data is accessed and modified in a controlled manner.
Dining Philosophers Problem
Dining Philosophers Problem
A classic problem used to illustrate the complexities of inter-process communication, where multiple philosophers try to eat from bowls of noodles but need to share chopsticks to do so.
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Semaphores
Semaphores
A common approach to handling concurrent access to shared resources. It provides a mechanism to protect critical sections and ensure that only one process can enter at a time.
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Layered Operating System
Layered Operating System
A type of operating system structure where components are organized in layers, with each layer building upon the one below. This modular approach promotes maintainability and code reuse.
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Threads
Threads
A mechanism that allows a single process to appear as multiple independent processes. It helps improve efficiency by utilizing the CPU more effectively.
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Process States
Process States
The various states a process can be in, including running, waiting, ready, and terminated. Transitions between these states are governed by the operating system.
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Abstract Data Type (ADT)
Abstract Data Type (ADT)
Represents a collection of data elements in a logical and structured manner, allowing for efficient manipulation and access.
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Encapsulation
Encapsulation
The ability to hide implementation details of an object, exposing only essential functionalities.
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Polymorphism
Polymorphism
The ability of an object to take on multiple forms or behaviors based on the context.
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Inheritance
Inheritance
The mechanism for creating new classes based on existing ones, inheriting properties and methods.
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Design Patterns
Design Patterns
Reusable solutions to common design problems, promoting code reusability and consistency.
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Object Identity
Object Identity
The capacity for an object to be uniquely identified, even if its state might be the same as another object.
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Concrete Invariant
Concrete Invariant
A function that governs the state of an object, ensuring it remains valid within its defined constraints.
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Abstraction Function
Abstraction Function
A function that maps an object's abstract representation to its concrete implementation.
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System Concept
System Concept
A structured way of organizing and representing information, with a focus on relationships between elements. It includes components, boundaries, and interactions within a system.
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Management Definition
Management Definition
A set of actions designed to achieve a specific goal, usually involving planning, organizing, leading, and controlling resources.
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Management Functions
Management Functions
The functions of management include setting goals, analyzing situations, making decisions, implementing plans, and monitoring progress.
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Bagging
Bagging
Bagging (Bootstrap Aggregating) is a machine learning technique that combines multiple decision trees to improve prediction accuracy. It reduces variance and overfitting by averaging individual tree predictions.
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K-Means Clustering
K-Means Clustering
An unsupervised learning algorithm used for grouping data points into clusters based on their similarities. It identifies 'natural patterns' in data by minimizing the distance between points within a cluster and maximizing the distance between clusters.
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Hierarchical Clustering
Hierarchical Clustering
A hierarchical clustering method that builds a dendrogram by iteratively merging clusters based on their similarity. It uses a distance metric to determine the closeness of clusters.
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K-Modes Clustering
K-Modes Clustering
A clustering technique specifically designed for categorical data. It uses the concept of frequency of categorical values to form clusters. K-Modes is similar to K-Means but for categorical variables.
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Association Rule Mining using FP-Growth
Association Rule Mining using FP-Growth
A technique for discovering association rules (relationships) within data. It uses the FP-Growth algorithm to efficiently find frequent itemsets and their associated patterns.
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Cloud Computing
Cloud Computing
A way of managing and delivering IT services, such as storage, computing power, and networking, over the internet instead of on physical hardware.
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Cloud Management
Cloud Management
A collection of technologies and services that enable businesses to manage their cloud infrastructure effectively. It involves tasks like resource allocation, security, monitoring, and cost optimization.
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Service Oriented Architecture (SOA)
Service Oriented Architecture (SOA)
A type of program architecture where services are broken down into smaller components that communicate with each other through messages. It allows for faster development and easier maintenance.
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Enterprise Service Bus (ESB)
Enterprise Service Bus (ESB)
A platform that helps connect different services and applications within an SOA. It provides a central bus for messages and communication.
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Cloud-based Storage
Cloud-based Storage
A type of cloud storage where data can be accessed and stored online, without the need for physical servers. It is typically managed by a third-party provider.
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Manned Cloud storage
Manned Cloud storage
A type of storage where data is stored and managed physically within a data center, requiring human intervention for its maintenance.
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Unmanned cloud storage
Unmanned cloud storage
A type of storage where data is stored and managed automatically by machines without human intervention. This allows for greater efficiency and scalability.
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Cloud Mail Services
Cloud Mail Services
Email services that are hosted online and accessible from anywhere with an internet connection, such as Gmail, Hotmail, and Yahoo Mail.
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Non-numeric data
Non-numeric data
Data that doesn't have numerical values, like categories or labels. Think of it like descriptive information.
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Decision trees
Decision trees
A flowchart-like structure used to make decisions based on data. It helps predict an outcome by asking a series of yes/no questions.
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Unsupervised learning
Unsupervised learning
Finding patterns and grouping similar data without any pre-defined labels. It's like sorting objects into piles based on their similarities.
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Criterion functions for clustering
Criterion functions for clustering
A function that measures how good a cluster is based on how similar items within a cluster are. It's like a scorecard for cluster quality.
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Algorithms for clustering
Algorithms for clustering
Popular algorithms used for unsupervised learning. K-means divides data into K clusters, while hierarchical methods create a tree-like structure of clusters.
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Graph theory
Graph theory
The study of graphs (networks of nodes and edges) with applications in various fields like computer science, social networks, and logistics.
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Cycle in a graph
Cycle in a graph
A sequence of nodes in a graph that visits each node exactly once, creating a closed loop.
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Shortest path problem
Shortest path problem
Finding the shortest path between two points in a graph, like finding the most efficient route on a map.
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Chromatic Number
Chromatic Number
The minimum number of colors needed to color the vertices of a graph so that no two adjacent vertices have the same color.
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Clique
Clique
A complete subgraph within a larger graph.
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Chordal Graph
Chordal Graph
A graph where every cycle has an edge connecting non-adjacent vertices within the cycle.
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Greedy Coloring Algorithm
Greedy Coloring Algorithm
A graph coloring algorithm where each vertex is assigned the lowest available color, starting from an arbitrary vertex.
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Brooks' Theorem
Brooks' Theorem
A theorem stating that the chromatic number of a graph is at most one more than the maximum degree of any vertex in the graph, except for complete graphs and odd cycles.
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Edge Colorings
Edge Colorings
An assignment of colors to the edges of a graph so that no two adjacent edges have the same color.
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Gupta-Vizing Theorem
Gupta-Vizing Theorem
A theorem stating that the chromatic index of a graph (minimum number of colors for edges) is either equal to the maximum degree of any vertex or one more than the maximum degree.
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Class-1 Graph
Class-1 Graph
A graph where the maximum degree of any vertex is equal to the chromatic index (minimum number of colors for edges).
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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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