Syllabus of B.tech. V SEM AIML (RGPV)
Syllabus of B. Tech. V Sem AIML (RGPV)
Syllabus of AL-501 Operating Systems
Source: (rgpv.ac.in)
UNIT-1 : Introduction to Operating Systems
Function, Evolution, Different types of Operating Systems,
Desirable Characteristics and features of an O/S.
Operating Systems Services: Types of Services,
Different ways of providing these Services– Commands, System Calls.
Need of System Calls, Low level implementation of System Calls,
Portability issue,
Operating System Structures.
UNIT-2 : File Systems (Secondary Storage Management)
File Concept, User’s and System Programmer’s view of File System,
Hard Disk Organization,
Disk Formatting and File System Creation,
Different Modules of a File System,
Disk Space Allocation Methods – Contiguous, Linked, Indexed.
Disk Partitioning and Mounting; Directory Structures, File Protection;
Virtual and Remote File Systems.
Case Studies of File Systems being used in Unix/Linux & Windows;
System Calls used in these Operating Systems for file management.
UNIT-3 : Process Management
Concept of a process, Process State Diagram,
Different type of schedulers, CPU scheduling algorithms, Evaluation of scheduling algorithms,
Concept of Threads: User level & Kernel level Threads, Thread Scheduling;
Multiprocessor/Multi core Processor Scheduling.
Case Studies of Process Management in Unix/Linux& Windows;
System Calls used in these Operating Systems for Process Management.
Concurrency & Synchronization:Real and Virtual Concurrency,
Mutual Exclusion, Synchronization,
Critical Section Problem, Solution to Critical Section Problem: Mutex Locks;
Monitors;
Semaphores,WAIT/SIGNAL operations and their implementation;
Classical Problems of Synchronization;
Inter-Process Communication.
Deadlocks:Deadlock Characterization, Prevention, Avoidance, Recovery.
UNIT-4 : Memory Management
Different Memory Management Techniques –Contiguous allocation;
Non-contiguous allocation: Paging, Segmentation, Paged Segmentation;
Comparison of these techniques.
Virtual Memory – Concept, Overlay, Dynamic Linking and Loading,
Implementation of Virtual Memory by Demand Paging etc.;
Memory Management in Unix/Linux& Windows.
UNIT-5 : Input/Output Management
Overview of Mass Storage Structures, Disk Scheduling;
I/O Systems: Different I/O Operations- Program Controlled,
Interrupt Driven, Concurrent I/O, Synchronous/Asynchronous and Blocking/Non-Blocking I/O Operations,
I/O Buffering,Application I/O Interface, Kernel I/O Subsystem,
Transforming I/O requests to hardware operations.
Overview of Protection & Security Issues and Mechanisms;
Introduction to Multiprocessor, Real Time, Embedded and Mobile Operating Systems;
Overview of Virtualization.
== END OF UNITS==
Syllabus of AL-502 Database Management Systems
Source: (rgpv.ac.in)
UNIT-1 :
:DBMS Concepts and architecture Introduction,
Database approach v/s Traditional file accessing approach, Advantages of database systems,
Data models, Schemes and instances,
Data independence, Data Base Language and interfaces,
Overall Database Structure,
Functions of DBA and designer,
ER data model: Entitles and attributes, Entity types, Defining the E-R diagram,
Concept of Generalization, Aggregation and Specialization.
Transforming ER diagram into the tables.
Various other data models object oriented data Model, Network data model, and Relational data model,
Comparison between the three types of models.
Storage structures: Secondary Storage Devices,
Hashing & Indexing structures: Single level & multilevel indices.
UNIT-2 :
Relational Data models: Domains, Tuples, Attributes, Relations, Characteristics of relations,
Keys, Key attributes of relation,
Relational database, Schemes, Integrity constraints.
Referential integrity, Intension and Extension,
Relational Query languages: SQLDDL, DML, integrity constraints, Complex queries, various joins,
indexing, triggers, assertions , Relational algebra and relational calculus,
Relational algebra operations like select, Project ,Join, Division, outer union.
Types of relational calculus i.e. Tuple oriented and domain oriented relational calculus and its operations.
UNIT-3 :
Data Base Design: Introduction to normalization,
Normal forms- 1NF, 2NF, 3NF and BCNF,
Functional dependency,Decomposition, Dependency preservation and lossless join,
problems with null valued and dangling tuples, multi valued dependencies.
Query Optimization: Introduction, steps of optimization,
various algorithms to implement select,
project and join operations of relational algebra,
optimization methods: heuristic based, cost estimation based.
UNIT-4 :
Transaction Processing Concepts: -Transaction System,
Testing of Serializability, Serializability of schedules, conflict & view serializable schedule, recoverability,
Recovery from transaction failures.
Log based recovery. Checkpoints deadlock handling.
Concurrency Control Techniques: Concurrency Control, locking Techniques for concurrency control, timestamping protocols for concurrency control,
validation based protocol, multiple granularity.
Multi version schemes, Recovery with concurrent transaction. Introduction to Distributed databases, data mining, data warehousing,
Object Technology and DBMS, Comparative study of OODBMS Vs DBMS .
Temporal, Deductive, Multimedia, Web & Mobile database. .
UNIT-5 :
Case Study of Relational Database Management Systems through Oracle/PostgreSQL /MySQL: Architecture, physical files,
memory structures, background process.
Data dictionary, dynamic performance view.
Security, role management, privilege management, profiles, invoke defined security model.
SQL queries, Hierarchical quires, inline queries, flashback queries.
Introduction of ANSI SQL,
Cursor management: nested and parameterized cursors.
Stored procedures, usage of parameters in procedures.
User defined functions their limitations. Triggers, mutating errors, instead of triggers.
== END OF UNITS==
Syllabus of AL-503 (A) Information Retrieval (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction - History of IR- Components of IR -
Issues -Open source Search engine Frameworks -
The Impact of the web on IR -
The role of artificial intelligence (AI) in IR – IR Versus Web Search -
Components of a search engine,
Characterizing the web.
UNIT-2 :
Boolean and Vector space retrieval models-
Term weighting - TF-IDF weighting cosine similarity -
Pre processing - Inverted indices -
efficient processing with sparse vectors Language Model based IR -
Probabilistic IR -Latent Semantic indexing - Relevance feedback and query expansion
UNIT-3 :
Web search overview,
web structure the user paid placement search engine optimization,
Web Search Architectures - crawling - meta-crawlers,
Focused Crawling - web indexes - Near duplicate detection - Index Compression - XML retrieval.
UNIT-4 :
Link Analysis -hubs and authorities - Page Rank and HITS algorithms -
Searching and Ranking -Relevance Scoring and ranking for Web -
Similarity - Hadoop & Map Reduce -
Evaluation -Personalized search -
Collaborative filtering and content-based recommendation of documents And products - handling invisible Web -
Snippet generation, Summarization.
Question Answering, Cross-Lingual Retrieval.
UNIT-5 :
Information filtering: organization and relevance feedback - Text Mining- Text classification and clustering -
Categorization algorithms, naive Bayes,
decision trees and nearest neighbor -
Clustering algorithms: agglomerative clustering,
k-means, expectation maximization (EM).
== END OF UNITS==
Syllabus of AL-503 (B) Deep Learning (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction History of Deep Learning,
McCulloch Pitts Neuron,
Multilayer Perceptions (MLPs), Representation Power of MLPs,
Sigmoid Neurons, Feed Forward Neural Networks,
Back propagation, weight initialization methods,
Batch Normalization, Representation Learning,
GPU implementation, Decomposition – PCA and SVD.
UNIT-2 :
Deep Feedforward Neural Networks,
Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD,
AdaGrad, Adam, RMSProp,
Auto-encoder, Regularization in auto-encoders, Denoising auto-encoders, Sparse auto-encoders,
Contractive auto-encoders,Variational auto-encoder,
Auto-encoders relationship with PCA and SVD,
Dataset augmentation.
Denoising auto encoders,
UNIT-3 :
Introduction to Convolutional neural Networks (CNN) and its architectures,
CCN terminologies: ReLu activation function, Stride, padding, pooling, convolutions operations,
Convolutional kernels, types of layers: Convolutional, pooling, fully connected, Visualizing CNN,
CNN examples: LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, RCNNetc.
Deep Dream, Deep Art.
Regularization: Dropout, drop Connect, unit pruning, stochastic pooling,
artificial data, injecting noise in input, early stopping,
Limit Number of parameters, Weight decay etc.
UNIT-4 :
Introduction to Deep Recurrent Neural Networks and its architectures,
Back propagation Through Time (BPTT),
Vanishing and Exploding Gradients,
Truncated BPTT, Gated Recurrent Units (GRUs),
Long Short Term Memory (LSTM),
Solving the vanishing gradient problem with LSTMs,
Encoding and decoding in RNN network,
Attention Mechanism, Attention over images, Hierarchical Attention,
Directed Graphical Models.
Applications of Deep RNN in Image Processing,
Natural Language Processing, Speech recognition, Video Analytics.
UNIT-5 :
Introduction to Deep Generative Models,
Restricted Boltzmann Machines (RBMs), Gibbs Sampling for training RBMs,
Deep belief networks, Markov Networks, Markov Chains,
Auto-regressive Models: NADE, MADE, PixelRNN,
Generative Adversarial Networks (GANs),
Applications of Deep Learning in Object detection,
speech/ image recognition, video analysis, NLP, medical science etc.
== END OF UNITS==
Syllabus of AL-503(C) Optimization Techniques in Machine Leaning (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction What is optimization,
Formulation of LPP, Solution of LPP: Simplex method,
Basic Calculus for optimization: Limits and multivariate functions,
Derivatives and linear approximations: Single variate functions and multivariate functions.
UNIT- 2 :
Machine Learning Strategy ML readiness,
Risk mitigation,
Experimental mindset, Build/buy/partner, setting up a team,
Understanding and communicating change
UNIT-3 :
Responsible Machine Learning AI for good and all,
Positive feedback loops and negative feedback loops,
Metric design and observing behaviours,
Secondary effects of optimization,
Regulatory concerns.
UNIT-4 :
Machine Learning in production and planning Integrating info systems,
users break things, time and space complexity in production,
when to retain the model?
Logging ML model versioning,
Knowledge transfer,
Reporting performance to stakeholders.
UNIT-5 :
Care and feeding of your machine learning model MLPL Recap,
Post deployment challenges,
QUAM monitoring and logging, QUAM Testing, QUAM maintenance, QUAM updating,
Separating Datastack from Production,
Dashboard Essentials and Metrics monitoring.
== END OF UNITS==
Syllabus of AL-504 (A) AI in Health Care (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Disease detection with computer vision Medical Image Diagnosis,
Eye Disease and Cancer Diagnosis,
Building and Training a Model for Medical Diagnosis,
Training, prediction, and loss,
Image Classification and Class Imbalance,
Generating More Samples, Model Testing
UNIT-2 :
Evaluating models Sensitivity,
Specificity, and Evaluation Metrics,
Accuracy in terms of conditional probability,
Confusion matrix, ROC curve and Threshold Image segmentation on MRI images Medical Image Segmentation, MRI Data and Image Registration,
Segmentation, 2-D U-Net and 3-D U-Net Data augmentation and loss function for segmentation,
Different Populations and Diagnostic Technology, External validation.
UNIT-3 :
Linear prognostic models Medical Prognosis,
Atrial fibrillation, Liver Disease Mortality, Risk of heart disease,
Evaluating Prognostic Models,
Concordant Pairs, Risk Ties, Permissible Pairs.
Prognosis with Tree-based models Decision trees for prognosis, fix over fitting,
Different distributions,Missing Data example, Imputation
UNIT-4 :
Survival Models and Time Survival Model,
Survival function, collecting time data, estimating the survival function.
Build a risk model using linear and tree-based models Hazard Functions,
Relative risk, Individual vs. baseline hazard,
Survival Trees,
Nelson Aalen estimator
UNIT-5 :
Medical Treatment Effect Estimation Analyze data from a randomized control trial,
Average treatment effect, Conditional average treatment effect,
T-Learner, S-Learner, C-forbenefit.
== END OF UNITS==
Syllabus of AL-504 (B) Natural Language Processing (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 : Introduction
Origins and challenges of NLP – Language Modeling: Grammar based LM,
Statistical LM – Regular Expressions,
Finite-State Auto mat – English Morphology,
Transducers for lexicon and rules,
Tokenization, Detecting and Correcting Spelling Errors,
Minimum Edit Distance.
UNIT-2 : Word Level Analysis
Un-smoothed N-grams, Evaluating N-grams,
Smoothing,
Interpolation and Back off – Word Classes, Part-of-Speech Tagging,
Rule-based, Stochastic and Transformation-based tagging,
Issues in PoS tagging – Hidden Markov and Maximum Entropy models,
Viterbi algorithms and EM training
UNIT-3 : Syntactic Analysis
Context-Free Grammars, Grammar rules for English,
Treebanks, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, Ambiguity,
Dynamic Programming parsing – Shallow parsing – Probabilistic CFG,
Probabilistic CYK, Probabilistic Lexicalized CFGs – Feature structures,
Unification of feature structures.
UNIT-4 : Semantics and Pragmatics
Requirements for representation,
First-Order Logic, Description Logics – Syntax-Driven Semantic analysis,
Semantic attachments – Word Senses, Relations between Senses,
Thematic Roles, selectional restrictions – Word Sense Disambiguation,
WSD using Supervised, Dictionary & Thesaurus,
Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.
Compositional semantics.
UNIT-5 : Application of NLP
intelligent work processors: Machine translation,
user interfaces,
Man-Machine interfaces,
natural language querying, tutoring and authoring systems,
speech recognition, and commercial use of NLP.
== END OF UNITS==
Syllabus of AL-504 (C) Computational Intelligence (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction to Computational Intelligence (CI): Basics of CI, History of CI,
Adaptation, Learning, Self-Organization, State Space Search and Evolution,
CI and Soft Computing, CI Techniques; Applications of CI;
Decision Trees: Introduction, Evaluation, Different splitting criterion,
Implementation aspect of decision tree.
Neural Network: Introduction, types, issues, implementation, applications
UNIT-2 :
Fuzzy Set Theory: Fuzzy Sets, Fuzzy Set Characteristics,
Basic Definition and Terminology, Fuzzy Operators,Fuzzy Relations and Composition,
Member Function Formulation, Fuzzy Rules and Fuzzy Reasoning,
Extension, Fuzzy Inference Systems,
Input Space Partitioning and Fuzzy Modeling.
Fuzziness and Defuzzification, Fuzzy Controllers,
Different Fuzzy Models: Mamdani Fuzzy Models, Sugeno Fuzzy Models,
Tsukamoto Fuzzy Models etc.
Neuro Fuzzy Modeling,
Introduction to Neuro Fuzzy Control
UNIT-3 :
Rough Set Theory: Introduction, Fundamental Concepts,
Knowledge Representation, Set Approximations and Accuracy,
Vagueness and Uncertainty in Rough Sets,
Rough Membership Function, Attributes Dependency and Reduction,
Application Domain, Hidden Markov Model (HMM),
Graphical Models, Variable Elimination, Belief Propagation,
Markov Decision Processes.
UNIT-4 :
Evolutionary Computation: Genetic Algorithms: Basic Genetics, Concepts, Working Principle, Creation of Off springs,
Encoding, Fitness Function, Selection Functions,
Genetic Operators-Reproduction, Crossover, Mutation;
Genetic Modeling, Benefits;
Problem Solving;
Introduction to Genetic Programming,
Evolutionary Programming, and Evolutionary Strategies.
UNIT-5 :
Swarm Intelligence: Introduction to Swarm Intelligence,
Swarm Intelligence Techniques: Ant Colony Optimization (ACO): Overview, ACO Algorithm;
Particle Swarm Optimization (PSO): Basics, Social Network Structures, PSO Parameters and Algorithm;
Grey wolf optimization(GWO);
Application Domain of ACO and PSO;
Bee Colony Optimization etc.;
Hybrid CI Techniques and applications;
CI Tools
== END OF UNITS==