UNIT-I
Machine Learning- Introduction, Types, Linear models, Introduction of Neural Network, Training a neural
network, Activation functions, Loss functions, Hyper parameters, Neural networks as universal function
approximates, Road to deep learning.
UNIT-II
Deep Networks : Introduction and history of deep learning, Probabilistic theory of deep learning, Common
architectural principles of deep networks, Building blocks of deep networks, Comparison with shallow networks,

UNIT-I
Introduction to Soft Computing: Introduction, Comparison with hard computing, Concept of learning and
adaptation, Constituents of soft computing, Applications of soft computing.
Artificial Neural Networks: Basic concepts of neural networks, Human brain, Biological neural network,
History of artificial neural networks, Basic building blocks of an artificial neuron, Neural network architectures,
Activation functions.
UNIT-II

UNIT-I
Introduction of Cloud : Definition, benefits, usage scenarios, History of Cloud Computing, Cloud Architecture,
Types of Clouds, Business models around Clouds, Major Players in Cloud Computing, Issues in Clouds,
Eucalyptus, Nimbus, Open Nebula, CloudSim.
UNIT-II
Cloud Services: Types of Cloud services- Software as a Service, Platform as a Service, Infrastructure as a
Service, Database as a Service, Monitoring as a Service, Communication as services. Service providers- Google,
Amazon, Microsoft Azure, IBM, Sales force.

UNIT-I
Understanding big data: What is big data, why big data, convergence of key trends, unstructured data, industry
examples of big data, web analytics, big data and marketing, fraud and big data, risk and big data ,credit risk
management, big data and algorithmic trading, big data and HealthCare, big data in medicine, advertising and big
data, big data technologies, Introduction to Hadoop, open source technologies, cloud and big data mobile business
intelligence, Crowd sourcing Analytics, inter and trans firewall analytics.

UNIT-I
Neurocomputing and Neuroscience: the human brain, biological neurons, neural processing, biological neural
network.
Artificial Neural Networks: introduction, historical notes, neuron model, knowledge representation, comparison
with biological neural network, applications.
Learning process: Supervised learning, unsupervised learning, Error correction learning, competitive learning,
adaptation learning, statistical nature of the learning process.
UNIT-II

UNIT-I
Introduction to Natural Language Understanding: The study of Language, Applications of NLP, Evaluating
Language Understanding Systems, Different levels of Language Analysis, Representations and Understanding,
Organization of Natural language Understanding Systems, Linguistic Background: An outline of English syntax.
UNIT-II
Introduction to semantics and knowledge representation, some applications like machine translation, database
interface.
UNIT-III

UNIT-I
Review of Software Engineering: Overview of software evolution, SDLC, Testing Process, Terminologies in
Testing: Error, Fault, Failure, Verification, Validation, Difference between Verification and Validation, Test
Cases, Testing Suite, Test Oracles, Impracticality of Testing All data; Impracticality of testing All Paths.
Verification: Verification methods, SRS verification, Source code reviews, User documentation verification, and
Software project audit, Tailoring Software Quality Assurance Program by Reviews, Walkthrough, Inspection, and

UNIT-I
Introduction to security attacks, Services and mechanism, Classical encryption techniques substitution ciphers and
transposition ciphers, Cryptanalysis, Steganography, Stream and block ciphers.
Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion and diffusion, Feistel structure,
Data encryption standard(DES), Strength of DES, Idea of differential cryptanalysis, Block cipher modes of
operations, Triple DES
UNIT-II

UNIT-I
Introduction: Introduction to Software Engineering, Software Components, Software Characteristics, Software
Crisis, Software Engineering Processes, Similarity and Differences from Conventional Engineering Processes,
Software Quality Attributes, Software Development Life Cycle (SDLC) Models- Water Fall Model, Prototype
Model, Spiral Model, Evolutionary Development Models, Iterative Enhancement Models.
UNIT-II
Software Requirement Specifications (SRS): Requirement Engineering Process: Elicitation, Analysis,

UNIT-I
Introduction and Line Generation: Types of computer graphics, Graphic Displays- Random scan displays,
Raster scan displays, Frame buffer and video controller, Points and lines, Line drawing algorithms, Circle
generating algorithms, Midpoint circle generating algorithm and parallel version of these algorithms.
UNIT-II
Transformations: Basic transformation, Matrix representations and homogenous coordinates, Composite
transformations, Reflections and shearing.