Semester 5 Probability & Statistics PDF

Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 20...

Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) SEMESTER –V Name of the Course: B. Tech in AI & ML Subject: Probability & Statistics Course Code: PCCAIML 501 Semester: V Teaching Scheme Maximum Marks: 100 Theory: 3 hrs./week Examination Scheme Tutorial: End Semester Exam: 70 Practical:0 Attendance: 5 Credit:3 Continuous Assessment: 25 Aim: Sl. No. 1. The aim of this course is to equip the students with standard concepts and tools at an intermediate to advanced level that will serve them well towards tackling various problems in the discipline. 2. The objective of this course is to familiarize the students with statistical techniques. Objective: Throughout the course, students will be expected to demonstrate their understanding of probability & statistics by being able to learn each of the following Sl. No. 1. The ideas of probability and random variables and various discrete and continuous probability distributions and their properties. 2. The basic ideas of statistics including measures of central tendency, correlation and regression. 3. The statistical methods of studying data samples. Pre-Requisite: Sl. No. 1. Knowledge of basic algebra, calculus. 2. Ability to learn and solve mathematical model.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Contents Hrs./we Contents ek Chapter Name of the Topic Hours Marks 01 Definition of Partial Differential Equations, First order partial 16 20 differential equations, solutions of first order linear PDEs; Solution to homogenous and nonhomogeneous linear partial differential equations of second order by complimentary function and particular integral method. Second-order linear equations and their classification, Initial and boundary conditions, D'Alembert's solution of the wave equation; Duhamel's principle for one dimensional wave equation. Heat diffusion and vibration problems, Separation of variables method to simple problems in Cartesian coordinates. The Laplacian in plane, cylindrical and spherical polar coordinates, solutions with Bessel functions and Legendre functions. One dimensional diffusion equation and its solution by separation of variables. 02 Probability spaces, conditional probability, independence; Discrete 16 25 random variables, Independent random variables, the multinomial distribution, Poisson approximation to the binomial distribution, infinite sequences of Bernoulli trials, sums of independent random variables; Expectation of Discrete Random Variables, Moments, Variance of a sum, Correlation coefficient, Chebyshev's Inequality. Continuous random variables and their properties, distribution functions and densities, normal, exponential and gamma densities.Bivariate distributions and their properties, distribution of sums and quotients, conditional densities, Bayes' rule. 03 Basic Statistics, Measures of Central tendency: Moments, skewness 16 25 and Kurtosis - Probability distributions: Binomial, Poisson and Normal - evaluation of statistical parameters for these three distributions, Correlation and regression – Rank correlation. Curve fitting by the method of least squares- fitting of straight lines, second degree parabolas and more general curves. Test of significance: Large sample test for single proportion, difference of proportions, Tests for single mean, difference of means, and difference of standard deviations. Test for ratio of variances - Chi- square test for goodness of fit and independence of attributes. Sub Total: 48 70 Internal Assessment Examination & Preparation of Semester 4 30 Examination Total: 52 100  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Assignments: Based on the curriculum as covered by subject teacher. List of Books Text Books: Name of Author Title of the Book Edition/ISSN/ISBN Name of the Publisher Erwin Kreyszig Advanced Engineering 9 th Edition John Wiley & Sons Mathematics N. G. Das Statistical Methods 0070083274, Tata Mc.Graw Hill 9780070083271 Reference Books: P. G. Hoel, S. C. Port Introduction to Universal Book Stall and C. J. Stone Probability Theory W. Feller An Introduction to 3rd Ed. Wiley Probability Theory and its Applications  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Operating Systems Code: PCC- CS502 Contacts: 3L Name of the Subject: Operating Systems Course Code: PCC-CS502 Semester: V Duration: 6 months Maximum Marks:100 Teaching Scheme Examination Scheme Theory:3 hrs./week Mid Semester exam: 15 Tutorial: NIL Assignment and Quiz: 10 marks Attendance : 5 marks Practical: hrs./week End Semester Exam :70 Marks Credit Points: 3 Unit Content Hrs/U Marks/ nit Unit Introduction: Concept of Operating Systems, 3 1 Generations of Operating systems, Types of Operating Systems, OS Services, System Calls, Structure of an OS - Layered, Monolithic, Microkernel Operating Systems, Concept of Virtual Machine. Case study on UNIX and WINDOWS Operating System. Processes: Definition, Process Relationship, Different 10 2 states of a Process, Process State transitions, Process Control Block (PCB), Context switching Thread: Definition, Various states, Benefits of threads, Types of threads, Concept of multithreads, Process Scheduling: Foundation and Scheduling objectives, Types of Schedulers, Scheduling criteria: CPU utilization, Throughput, Turnaround Time, Waiting Time, Response Time; Scheduling algorithms: Pre- emptive and Non pre-emptive, FCFS, SJF, RR; Multiprocessor scheduling: Real Time scheduling: RM and EDF. Inter-process Communication: Critical Section, Race 3. Conditions, Mutual Exclusion, Hardware Solution, 5 Strict Alternation, Peterson’s Solution, The Producer Consumer Problem, Semaphores, Event Counters, Monitors, Message Passing, Classical IPC Problems: Reader’s & Writer Problem, Dinning Philosopher Problemetc. 4. Deadlocks: Definition, Necessary and sufficient 5 conditions for Deadlock, Deadlock Prevention, Deadlock Avoidance: Banker’s algorithm, Deadlock detection and Recovery.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) 5. Memory Management: Basic concept, Logical and 8 Physical address map, Memory allocation: Contiguous Memory allocation– Fixed and variable partition– Internal and External fragmentation and Compaction; Paging: Principle of operation –Page allocation Hardware support for paging, Protection and sharing, Disadvantages of paging. Virtual Memory: Basics of Virtual Memory – Hardware and control structures – Locality of reference, Page fault , Working Set , Dirty page/Dirty bit – Demand paging, Page Replacement algorithms: Optimal, First in First Out (FIFO), Second Chance (SC), Not recently used (NRU) and Least Recently used(LRU). 6. I/O Hardware: I/O devices, Device controllers, Direct 6 memory access Principles of I/O Software: Goals of Interrupt handlers, Device drivers, Device independent I/O software, Secondary-Storage Structure: Disk structure, Disk scheduling algorithms File Management: Concept of File, Access methods, File types, File operation, Directory structure, File System structure, Allocation methods (contiguous, linked, indexed), Free-space management (bit vector, linked list, grouping), directory implementation (linear list, hash table), efficiency andperformance. Disk Management: Disk structure, Disk scheduling - FCFS, SSTF, SCAN, C-SCAN, Disk reliability, Disk formatting, Boot-block, Bad blocks Text book and Reference books: 1. Operating System Concepts Essentials, 9th Edition by AviSilberschatz, Peter Galvin, Greg Gagne, Wiley Asia StudentEdition. 2. Operating Systems: Internals and Design Principles, 5th Edition,William Stallings, Prentice Hall of India. 3. Operating System Concepts, Ekta Walia, Khanna PublishingHouse (AICTE Recommended Textbook – 2018) 4. Operating System: A Design-oriented Approach, 1st Edition byCharles Crowley, Irwin Publishing 5. Operating Systems: A Modern Perspective, 2nd Edition by Gary J. Nutt,Addison- Wesley 6. Design of the Unix Operating Systems, 8th Edition by MauriceBach, Prentice-Hall of India 7. Understanding the Linux Kernel, 3rd Edition, Daniel P. Bovet,Marco Cesati, O'Reilly and Associates  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Operating System Lab Code: PCC- CS592 Contacts: 4P Name of the Course: Operating System Lab Course Code: PCC- CS592 Semester: V Duration:6 months Maximum Marks:100 Teaching Scheme: Theory: hrs./week Continuous Internal Assessment Tutorial: NIL External Assesement:60 Practical: 4 hrs./week Distribution of marks:40 Credit Points: 2 1 1. Managing Unix/Linux Operating System [8P]: Creating a bash shell script, making a script executable, shell syntax (variables, conditions,control structures, functions, commands). Partitions, Swap space, Device files, Raw and Block files, Formatting disks,Making file systems, Superblock, I-nodes, File system checker, Mounting file systems, Logical Volumes, Network File systems, Backup schedules and methods Kernel loading, init and the inittab file, Run-levels, Run level scripts. Password file management, Password security, Shadow file, Groups and the group file, Shells, restricted shells, user-management commands, homes and permissions, default files, profiles, locking accounts, setting passwords, Switching user, Switching group, Removing users &user groups. 2. Process [4P]: starting new process, replacing a process image, duplicating aprocess image, waiting for a process, zombie process. 3. Signal [4P]: signal handling, sending signals, signal interface, signal sets. 4. Semaphore [6P]: programming with semaphores (use functions semctl, semget, semop, set_semvalue, del_semvalue, semaphore_p, semaphore_v). 5. POSIX Threads [6P]: programming with pthread functions (viz. pthread_create, pthread_join, pthread_exit, pthread_attr_init, pthread_cancel) 6. Inter-process communication [6P]: pipes(use functions pipe, popen, pclose), named pipes(FIFOs, accessing FIFO), message passing & shared memory(IPC version V). Any experiment specially designed by the college (Detailed instructions for Laboratory Manual to be followed for further guidance)  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Object Oriented Programming Code: PCC-CS503 Contacts: 3L Name of the Subject: Object Oriented Programming Course Code: PCC-CS 503 Semester: V Duration:6 months Maximum Marks:100 Teaching Scheme Examination Scheme Theory:3 hrs./week Mid Semester exam: 15 Tutorial: NIL Assignment and Quiz : 10 marks Attendance: 5 marks Practical: hrs./week End Semester Exam:70 Marks Credit Points: 3 Unit Content Hrs/Unit Marks/Unit Abstract data types and their 8 1 specification.How to implement an ADT. Concrete state space, concrete invariant, abstraction function. Implementingoperations, illustrated by the Text example. 2 Features of object-oriented programming. 8 Encapsulation, object identity, polymorphism –but not inheritance. 3 Inheritance in OO design. 6 Design patterns. Introduction and classification. Theiterator pattern. 4 Model-view-controller pattern. 6 Commands as methods and as objects. ImplementingOO language features. Memory management. 5 Generic types and collections 6 GUIs. Graphical programming with Scale and Swing. The software development process Text book and Reference books: 1. Rambaugh, James Michael, Blaha – "Object Oriented Modelling and Design" – Prentice Hall, India 2. Ali Bahrami – "Object Oriented System Development" – Mc Graw Hill 3. Patrick Naughton, Herbert Schildt – "The complete reference-Java2" – TMH 4. R.K Das – "Core Java For Beginners" – VIKAS PUBLISHING 5. Deitel and Deitel – "Java How to Program" – 6th Ed. – Pearson 6. Ivor Horton's Beginning Java 2 SDK – Wrox 7. E. Balagurusamy – " Programming With Java: A Primer" – 3rd Ed. – TMH  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Object Oriented Programming & Java Lab Code: PCC-CS593 Contacts: 4P Name of the Course: Object Oriented Programming Lab Course Code: PCC- Semester:V CS593 Duration:6 months Maximum Marks:100 Teaching Scheme: Theory: hrs./week Continuous Internal Assessment Tutorial: NIL External Assesement:60 Practical: 4 hrs./week Distribution of marks:40 Credit Points: 2 Laboratory Experiments: 1. Assignments on class, constructor, overloading, inheritance, overriding 2. Assignments on wrapper class, arrays 3. Assignments on developing interfaces- multiple inheritance, extending interfaces 4. Assignments on creating and accessing packages 5. Assignments on multithreaded programming 6. Assignments on applet programming Note: Use Java for programming Any experiment specially designed by the college (Detailed instructions for Laboratory Manual to be followed for further guidance)  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Subject Code Subject Name L T P C PCCAIML 502 Introduction to Machine Learning 3 0 0 3 Pre-requisite NIL Course Objectives: 1. Ability to comprehend the concept of supervised and unsupervised learning techniques 2. Differentiate regression, classification and clustering techniques and to implement their algorithms. 3. To analyze the performance of various machine learning techniques and to select appropriate featuresfor training machine learning algorithms. Expected Course Outcome: 1. Understand the concepts of various machine learning strategies. 2. Handle computational data and learn ANN learning models. 3. Solve real world applications by selecting suitable learning model. 4. Boost the performance of the model by combining results from different approaches. 5. Recognize and classify sequencing patterns using HMM. 6. Infer the association and relationship between the data objects. 7. Construct machine learning model for unseen data and can solve real world application. Module:1 Introduction to Machine Learning 3 hours Introduction to Machine Learning (ML); Feature engineering; Learning Paradigm, Generalization of hypothesis, VC Dimension, PAC learning, Applications of ML. Module:2 Data Handling and ANN 4 hours Feature selection Mechanisms, Imbalanced data, Outlier detection- Artificial neural networks including backpropagation- Applications Module:3 ML Models and Evaluation 6 hours Regression: Multi-variable regression; Model evaluation; Least squares regression; Regularization; LASSO; Applications of regression, Classification – KNN, Naïve Bayes, SVM, Decision Tree; Training and testing classifier models; Cross-validation; Model evaluation (precision, recall, F1-mesure, accuracy, area under curve); Statistical decision theory including discriminant functions and decision surfaces Module:4 Model Assessment and Inference 4 hours Model assessment and Selection – Ensemble Learning – Boosting, Bagging, Model Inference and Averaging, Bayesian Theory, EM Algorithm Module:5 Hidden Markov Models 3 hours Hidden Markov Models (HMM) with forward-backward and Vierbi algorithms; Sequence classification using HMM; Conditional random fields; Applications of sequence classification such as part-of-speech tagging Module:6 Association Rules 3 hours  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Mining Association Rules in Large Databases. Mining Frequent Patterns-- basic concepts - Efficient and scalable frequent item set mining -methods, Apriori algorithm, FP-Growth algorithm Module:7 Clustering 5 hours K Means, Hierarchical Clustering – Single, complete, Average linkage; Ward’s algorithm; Minimum spanning tree clustering; BIRCH clustering Module:8 Recent Trends 2 hours Recent Trends and case study Total Lecture hours: 30 hours Text Book(s) 1. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Pearson, Third Edition, 2014. 2. Friedman Jerome, Trevor Hastie, and Robert Tibshirani. The Elements of Statistical Learning. Springer-Verlag, 2nd Edition, 2013. Reference Books 1. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012. 2. Peter Flach, “Machine Learning: The Art and Science of Algorithms that Make Sense of Data”, Cambridge University Press, 2012.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Subject Code Subject Name L T P C PCCAIML 592 Machine Learning Lab 0 0 4 2 Pre-requisite NIL Lab Experiments 1. Implement Decision Tree learning 2 hours 2. Implement Logistic Regression 2 hours 3. Implement classification using Multilayer perceptron 2 hours 4. Implement classification using SVM 2 hours 5. Implement Adaboost 2 hours 6. Implement Bagging using Random Forests 2 hours 7. Implement K‐means Clustering to Find Natural Patterns in Data 2 hours 8. Implement Hierarchical clustering 2 hours 9. Implement K‐mode clustering 2 hours 10 Implement Association Rule Mining using FP Growth 2 hours 11. Classification based on association rules 2 hours 12. Implement Gaussian Mixture Model Using the Ex ectation Maximization 2 hours 13 Evaluating ML algorithm with balanced and unbalanced datasets 2 hours 14 Comparison of Machine Learning algorithms 2 hours 15 Implement k‐nearest neighbour algorith 2 hours Total Lecture hours: 30 hours  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Introduction to Industrial Management (Humanities III) Code: HSMC-501 Contacts: 3L Name of the Course: Introduction to Industrial Management (Humanities III) Course Code: HSMC-501 Semester: V Duration:6 months Maximum Marks:100 Teaching Scheme Examination Scheme Theory:2 hrs./week Mid Semester exam: 15 Tutorial: NIL Assignment and Quiz : 10 marks Attendance: 5 marks Practical: NIL End Semester Exam:70 Marks Credit Points: 2 Unit Content Hrs/Unit Marks/Unit Introduction 6 1 System- concept, definition, types, parameters, variables and behavior. Management – definition andfunctions. Organization structure: i. Definition. ii. Goals. iii. Factors considered in formulatingstructure. iv. Types. v. Advantages and disadvantages. vi. Applications. Concept, meaning and importance of division of labor, scalar & functional processes, span of control, delegation ofauthority, centralization and decentralization in industrial management. Organizational culture and climate –meaning, differences and factors affecting them. Moral-factors affecting moral. Relationship between moral andproductivity. Job satisfaction- factors influencing jobsatisfaction. Important provisions of factory act andlabor laws.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) 2 Critical Path Method (CPM) and 8 Programme Evaluation Review Technique (PERT): 2.1 CPM & PERT-meaning, features, difference, applications. 2.2 Understand different terms used in network diagram. Draw network diagram for a real life project containing 10-15 activities, computation of LPO and EPO.(Take minimum three examples). Determination of critical path on network. Floats, its types and determination of floats. Crashing of network, updating and its applications. 3 Materials Management: 6 Material management-definition, functions, importance, relationship with other departments. Purchase - objectives, purchasing systems, purchase procedure, terms and forms used in purchase department. Storekeeping- functions, classification of stores as centralized and decentralized with their advantages, disadvantages and application in actual practice. Functions of store, types of records maintained by store, various types and applications of storage equipment, need and general methods for codification of stores. Inventory control: i. Definition. ii. Objectives. iii. Derivation for expression for Economic Order Quantity (EOQ) and numeric examples. iv. ABC analysis and other modern methods of analysis. v. Various types of inventory models such as Wilson’s inventory model, replenishment model and two bin model. (Only sketch and understanding, no derivation.). 3.6 Material Requirement Planning (MRP)- concept, applications and brief details about software packages available in market.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) 4 Production planning and Control 8 (PPC): Types and examples of production. PPC : i. Need and importance. ii. Functions. iii. Forms used and their importance. iv. General approach foreach type of production. Scheduling- meaning and need forproductivity and utilisation. Gantt chart- Format and method toprepare. Critical ratio scheduling-method andnumeric examples. Scheduling using Gantt Chart (for at least 5-7 components having 5-6 machining operations, with processes, setting and operation time for each component and process, resources available, quantity and other necessarydata), At least two examples. 4.7 Bottlenecking- meaning, effect andways to reduce. 5 Value Analysis (VA) and Cost Control: 4 5.1 VA-definition, terms used, process and importance. 5.2 VA flow diagram. DARSIRI method of VA. Case study of VA-at least two. Waste-types, sources and ways to reduce them. Cost control-methods and important guide lines. 6 Recent Trends in IM: 4 ERP (Enterprise resource planning) - concept, features and applications. Important features of MS Project. Logistics- concept, need and benefits. Just in Time (JIT)-concept and benefits. Supply chain management-concept and benefits. Text book and Reference books: 1. L.S. Srinath– “CPM & PERT principles and Applications”. 2. Buffa – “Modern Production Management”. 3. N. Nair – “Materials Management”. 4. O. P. Khanna – “ Industrial Engineering & Management”. 5. Mikes – “Value Analysis”. 6. S.C. Sharma, “Engineering Management – Industrial Engineering &Management”, Khanna Book Publishing Company, New Delhi  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Cloud Computing Code: PECAIML501A Contact: 3L Name of the Course: Cloud Computing Course Code: PECAIML501A Semester: V Duration: 6 months Maximum Marks: 100 Teaching Scheme Examination Scheme Theory: 3 hrs./week Mid Semester exam: 15 Tutorial: NIL Assignment and Quiz: 10 marks Attendance: 5 marks Practical: End Semester Exam: 70 Marks Credit Points: 3 Unit Content Hrs/ Marks/Unit Unit Definition of Cloud Computing and itsBasics 1 (Lectures ). Defining a Cloud, 9 Cloud Types – NIST model, Cloud Cube model, Deployment models (Public , Private, Hybrid and Community Clouds), Service Platform as a Service, Software asa Service with examples of services/ service providers, models – Infrastructure as a Service, Cloud Reference model, Characteristics of Cloud Computing – a shift in paradigm Benefits and advantages of Cloud Computing, A brief introduction on Composability, Infrastructure, Platforms, Virtual Appliances, Communication Protocols, Applications, Connecting to the Cloud by Clients, IaaS –Basic concept, Workload, partitioning of virtual private server instances, Pods, aggregations, silos PaaS – Basic concept, tools and development environment with examples SaaS - Basic concept and characteristics,Open SaaS and SOA, examples of SaaS platform Identity as a Service (IDaaS) Compliance as a Service (CaaS)  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Use of Platforms in Cloud Computing Concepts of 12 2 Abstraction and Virtualization Virtualization technologies : Typesofvirtualization (access, application, CPU,storage), Mobility patterns (P2V, V2V, V2P, P2P, D2C, C2C, C2D, D2D) Load Balancing and Virtualization: Basic Concepts, Network resources for load balancing, Advanced load balancing (including ApplicationDelivery Controller and Application Delivery Network),Mention of The Google Cloud as an example of use of load balancing Hypervisors: Virtual machine technology and types, VMware vSphere Machine Imaging (including mention of Open Virtualization Format – OVF) Porting of applications in the Cloud: The simple Cloud API and AppZero Virtual Application appliance, Concepts of Platform as a Service, Definition of services, Distinction between SaaS and PaaS (knowledge of Salesforce.com and Force.com), Application development Use of PaaS Application frameworks, Discussion of Google Applications Portfolio – Indexed search, Dark Web, Aggregation and disintermediation, Productivity applications and service, Adwords, Google Analytics, Google Translate, a brief discussion on Google Toolkit (including introduction of Google APIs in brief), major features of Google App Engine service., Discussion of Google Applications Portfolio – Indexed search, Dark Web, Aggregation and disintermediation, Productivity applications and service, Adwords, Google Analytics, Google Translate, a brief discussion on Google Toolkit (including introduction of Google APIs in brief), major features of Google App Engine service, Windows Azure platform: Microsoft’s approach, architecture, and main elements, overview of Windows Azure AppFabric, Content Delivery Network, SQL Azure, and Windows Live services,  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Cloud Infrastructure: 7 3 Cloud Management: An overview of the features of network management systems and a brief introduction of related products from large cloud vendors, Monitoring of an entire cloud computing deployment stack – an overview with mention of some products, Lifecycle management of cloud services (six stages of lifecycle). Concepts of Cloud Security: Cloud security concerns, Security boundary,Security service boundary Overview of securitymapping Security of data: Brokered cloudstorage access, Storage location and tenancy,encryption, and auditing and compliance Identity management (awareness of Identityprotocol standards) Concepts of Services and Applications : 8 4. Service Oriented Architecture: Basic conceptsof message-based transactions, Protocol stackfor an SOA architecture, Event-driven SOA, Enterprise Service Bus, Service catalogs, Applications in the Cloud: Concepts of cloud transactions, functionality mapping, Application attributes, Cloud service attributes, System abstraction and Cloud Bursting, Applications and Cloud APIs Cloud-based Storage: Cloud storage definition – Manned and Unmanned Webmail Services: Cloud mail services including Google Gmail, Mail2Web, WindowsLive Hotmail, Yahoo mail, concepts of Syndication services Text book and Reference books: 1. Cloud Computing Bible by Barrie Sosinsky, Wiley India Pvt. Ltd, 2013 2. Mastering Cloud Computing by Rajkumar Buyya, Christian Vecchiola, S. Thamarai Selvi, McGraw Hill Education (India) Private Limited,2013 3. Cloud computing: A practical approach, Anthony T. Velte, Tata Mcgraw- Hill 4. Cloud Computing, Miller, Pearson 5. Building applications in cloud:Concept, Patterns and Projects, Moyer, Pearson 6. Cloud Computing – Second Edition by Dr. Kumar Saurabh, Wiley India  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Pattern Recognition Code: PECAIML501B Contact: 3L Name of the Subject: Pattern Recognition Course Code: PECAIML501B Semester: V Duration:6 months Maximum Marks:100 Teaching Scheme Examination Scheme Theory:3 hrs./week Mid Semester exam: 15 Tutorial: NIL Assignment and Quiz: 10 marks Attendance: 5 marks Practical: NIL End Semester Exam:70 Marks Credit Points: 3 Unit Content Hrs/Unit Marks/Unit 1 Basics of pattern recognition 2 Bayesian decision theory 8L 8 2 Classifiers, Discriminant functions, Decision surfaces Normal density and discriminant functions Discrete features Parameter estimation methods 6L 6 3 Maximum-Likelihood estimation Gaussian mixture models Expectation-maximization method Bayesian estimation Hidden Markov models for sequential pattern 8 4. classification 8L Discrete hidden Markov models Continuous density hidden Markov models 5 Dimension reduction methods 3L 3 5.1. Fisher discriminant analysis 5.2Principal component analysis. Parzen-window method K-Nearest Neighbour method 6 Non-parametric techniques for density 2 estimation 7 Linear discriminant function based classifier 5L 5 Perceptron Support vector machines  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) 8 Non-metric methods for pattern classification 4L 4 Non-numeric data or nominal data Decision trees 9 Unsupervised learning and clustering 2L 2 Criterion functions for clustering Algorithms for clustering: K-means, Hierarchical and other methods Text book and Reference books: 1. R. O. Duda, P. E. Hart and D. G. Stork: Pattern Classification, John Wiley, 2001. 2. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th Ed., Academic Press, 2009. 3. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. Subject: Graph Theory Course Code: PECAIML501C Semester: V Maximum Marks: 100 Teaching Scheme Examination Scheme Theory: 3 hrs./week End Semester Exam: 70 Tutorial: Attendance : 5 Practical: 0 Continuous Assessment: 25 Credit: 3 Practical Sessional internal continuous evaluation: NA Practical Sessional external examination: NA Aim: Sl. No. 1. Understand the basic of graph theory. 2. Understand path, walks and cycle 3. Understand set covering and matches. 4. Understand vertex coloring. Objective: Sl. No. 1. To learn about the vertex, edge, path and cycle. 2. To learn about connected graph. 3. To learn about shortest path. 4. To learn about set covering and matching. 5. To learn about vertex coloring. Pre-Requisite:  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Sl. No. None Contents 4 Hrs./week Chapter Name of the Topic Hours Marks 01 Introduction 7 14 Discovery of graphs, Definitions, Subgraphs, Isomorphic graphs, Matrix representations of graphs, Degree of a vertex, Directed walks, paths and cycles, Connectivity in digraphs, Eulerian and Hamilton digraphs, Eulerian digraphs, Hamilton digraphs, Special graphs, Complements, Larger graphs from smaller graphs, Union, Sum, Cartesian Product, Composition, Graphic sequences, Graph theoretic model of the LAN problem, Havel- Hakimi criterion, Realization of a graphic sequence. 02 Connected graphs and shortest paths 7 14 Walks, trails, paths, cycles, Connected graphs, Distance, Cut- vertices and cut-edges, Blocks, Connectivity, Weighted graphs and shortest paths, Weighted graphs, Dijkstra’s shortest path algorithm, Floyd-Warshall shortest path algorithm. 03 Trees 7 14 Definitions and characterizations, Number of trees, Cayley’s formula, Kircho-matrix-tree theorem, Minimum spanning trees, Kruskal’s algorithm, Prim’s algorithm, Special classes of graphs, Bipartite Graphs, Line Graphs, Chordal Graphs, Eulerian Graphs, Fleury’s algorithm, Chinese Postman problem, Hamilton Graphs, Introduction, Necessary conditions and sufficient conditions. 04 Independent sets coverings and matchings 8 14 Introduction, Independent sets and coverings: basic equations, Matchings in bipartite graphs, Hall’s Theorem, K¨onig’s Theorem, Perfect matchings in graphs, Greedy and approximation algorithms.  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) 05 Vertex Colorings 7 14 Basic definitions, Cliques and chromatic number, Mycielski’s theorem, Greedy coloring algorithm, Coloring of chordal graphs, Brooks theorem, Edge Colorings, Introduction and Basics, Gupta-Vizing theorem, Class-1 and Class-2 graphs, Edge- coloring of bipartite graphs, Class-2 graphs, Hajos union and Class-2 graphs, A scheduling problem and equitable edge- coloring. Sub Total: 36 70 Internal Assessment Examination & Preparation of Semester 4 30 Examination Total: 40 100 List of Books Text Books: Name of Author Title of the Book Edition/ISSN/ISBN Name of the Publisher J. A. Bondy and U. S. Graph Theory 1 edition st Springer R. Murty Richard J. Trudeau Introduction to Graph 2nd edition Dover Publications Theory Reference Books: Chartrand and A First Course in ISBN-10: 0486483681 Dover Publications Zhang Graph Theory ISBN-13: 978- 0486483689 Maarten van Steen Graph Theory and ISBN-10: 9081540610 Maarten van Steen Complex Networks: An ISBN-13: 978- Introduction 9081540612 End Semester Examination Scheme. Maximum Marks-70. Time allotted- 3hrs. Group Unit Objective Questions Subjective Questions (MCQ only with the correct answer) No of Total No of To Marks Total question Marks question answer per Marks to be set to be set question A 1 to 5 10 10 B 1 to 5 5 3 5 60 C 1 to 5 5 3 15  Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly West Bengal University of Technology) Syllabus for B. Tech in Artificial Intelligence and Machine Learning (Applicable from the academic session 2020-2021) Only multiple choice type questions (MCQ) with one correct answer are to be set in the objective part. Specific instruction to the students to maintain the order in answering objective questions should be given on top of the question paper. Examination Scheme for end semester examination: Group Chapter Marks of each Question to be Question to be question set answered A All 1 10 10 B All 5 5 3 C All 15 5 3