Fundamental Concepts of AI:Agents, environments,general model; Problem Solving techniques. AI Fundamentals, Search Techniques and Knowledge Representation,ML Fundamentals, Machine Learning Algorithms and Neural Network and its Applications.

The objectives of the FDP is to enable theoretical and practical understanding of the most popular machine learning and deep learning tools and techniques, via expert lectures and hands on sessions, led by academicians.

History of Python, Installing Python, Executing Python Programs, Internal Working of Python, Python Implementations. Python Character Set, Token, Python Core Data Type, print function, Assigning Value to Variable, input function, eval function, Formatting Number and Strings, Operators and Expressions.

The main objective of this course is to provide theoretical and practical concept of fuzzy set and fuzzy logics. The participants will learn how to choose the appropriate fuzzy set and algorithm design method for a specified application.

Understand the research perspectives of WSN and IoT . Recognize the different aspects of security and machine learning in the IoT and WSN applications. Know how to apply various available tools related to these domains.

Fundamental Concepts of AI: Agents, environments, general model; Problem Solving techniques. AI Fundamentals, Search Techniques and Knowledge Representation, ML Fundamentals, Machine Learning Algorithms, and Neural Network and its Applications.

Fundamental Concepts of AI: Agents, environments, general model; Problem Solving techniques. AI Fundamentals, Search Techniques and Knowledge Representation, ML Fundamentals, Machine Learning Algorithms, and Neural Network and its Applications.

The participants will learn how the choice of data structures and algorithm design methods impacts the performance of programs. How to choose the appropriate data structure and algorithm design method for a specified application. Topic Covered: Basic Linear Data Structure (Array, Linked List etc). Basic Algorithms design Techniques, Divide and Conquer, greedy Technique, Dynamic Programming, Backtracking , P-NP Problems

The objective of this course is to familiarize participants with theoretical and practical concepts of Distributed Systems. Topics Covered: Motivation: What is a Distributed System, and why is it important? Tolerating Crash failures and Byzantine Failures, Recent Trends: How Clouds and Mobile Devices changed Distributed Computing.

IoT and cloud computing has a complementary relationship. While IoT generates large amounts of data, many cloud providers allow data transfer via the internet, that means facilitates a way to navigate the data. Cloud computing helps to collaborate in IoT development. Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data. Cloud Computing providers often utilize a “software as a service” model to allow customers to easily process data.

Big Data is a term that defines the huge volume of data that is growing exponentially. Data analytics includes extracting the useful information from the data by building all possible relations among various data. While machine learning in data analytics has helped manage big data better. Consider a situation where you have to collect huge amounts of data which is a cumbersome and time-consuming process.

The Internet of things refers to a type of network to connect anything with the Internet based on stipulated protocols through information sensing equipment’s to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring, and administration. In this FDP briefly discussed about what IOT is, how IOT enables different technologies, about its architecture, characteristics & applications, IOT functional view & what are the future challenges for IOT.

Introduction to IoT, current products, open source electronics for IoT, IoT language and its tools, communication protocols, interconnections, system design and developments, latest sensors and actuators, signal processing applications in IoT, research issues and case studies in IoT, hands-on training sessions will be provided provide with demos on real-time applications.

Introduction to Data Communication,Protocols and Standards: Protocols, Standards, Standards Organizations, Internet Standards, Packet Switching, Circuit Switching, A Network of Networks; Understanding of Delay, Loss and Throughput in the packet switched networks; Protocols layers and their service model: OSI, TCP/IP, X.25; TCP/IP vs OSI.

Introduction to Web Page Designing:History of Web and Internet, Protocols governing Web, Introduction to Internet services and tools, Introduction to clientserver computing. What is HTML? HTML Documents, Basic structure of an HTML document, Creating an HTML document, Markup Tags, Heading- Paragraphs, list, table, images, frames, forms, CSS, Document type definition, Creating Style Sheet, CSS Properties, CSS Styling and Pedagogy, Soft Skills & Demo Teaching by Participants.

Latex (Document Presentation System), MS Office (Suite of Products By Microsoft), Web Browsers & Its security, Social Media & Its security aspects, Mobile Apps For Indian Citizens, E-Commerce & M-Commerce, Internet Banking & Mobile Banking, Digital Payment Platforms, Paying bills, booking tickets and shopping on sites like Paytm, Freecharge, PayUmoney, PayPal etc.

Introduction to Database Systems; Levels of abstraction, Three layer architecture. Data Independence. Schema, Structure of DBMS. Data Models - Relational Data Model and comparison with other data models. Functions of Database Administration, Roles, managing data security, back up databases, controlling concurrent access, data quality management, tuning of database performance. Pedagogy, Soft Skills & Demo Teaching by Participants.

Introduction to digital computers, Revision of C language fundamentals with some tricky examples and implementations. Dynamic memory allocation, pointers, structures, unions, pointer and array, pointer to pointer, pointer to structure, pointers and functions, Header files and libraries, writing and using Makefile Programming will be in Linux environment where program editors like vim and emacs along with compilers like gcc, g++ will be used. gdb, valgrind will be used to demonstrate program debugging. Pedagogy, Soft skills & Demo Teaching by Participants.

Object Oriented Programming (OOPs), refers to a type of computer programming (software design) in which programmers define the data type of a data structure, and also the types of operations (functions) that can be applied to the data structure. In this way, the data structure becomes an object that includes both data and functions. In addition, programmers can create relationships between one object and another. For example, objects can inherit characteristics from other objects.

Java is a language which is used at a major scale for the development of mobile applications. Some of the other languages are there which can be used to develop the Android application like C and C++ using Native Development Kit (NDK), but Java is recommended to the applications. It supports native code also, so C and C++ are applicable to code for the mobile application. An SDK (Software Development Kit) is used for Android development which has a bunch of libraries instead of JVM. Before going for learning Android Development courses one should attain complete understanding on Java as it has a significant role in Android App development.

The objective of the course is two-fold. Firstly, it aims to provide an overview of hybrid computational intelligence and its applications to engineering problems through extensive deliberations followed by hands-on session on selected topics. Secondly, it is aimed to enlighten the intended participants with the intricacies of hybrid computational intelligence and to help them out in implementing the hybrid computational algorithms so that they get inspired to pursue further research avenues in this field thereby enriching it.

Introduction to Deep Learning and Motivation.Brief introduction of Artificial Neural Networks (ANN), Perceptrons, Multilayer perceptron (MLP), Back propagation training for MLP, Stochastic gradient descent. Applications to some practical classification problems. Hands on: Demonstration and implementation of Shallow and Deep architecture, introduction to Python, Tensorflow and Keras.

Image and Vision Computing has a primary aim of aim affective medium of interchange for results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. Its includes various fields like image interpretation, scene modeling, object recognition and tracking, shape analy- sis, monitoring and surveillance, biologically-inspired computer vision, motion analysis, stereo vision, data fusion from multiple sensor inputs, image databases ect.

Introduction to computer networks, TCP/IP model. Introduction to Security, Basic Concepts - Network layers, attacks and securities. Network attacker and malware attacker, application layer protocols - HTTP, SMTP and etc. Document Object Model (DOM), Port scanning behind the firewall. Browser security, Communication, security user interface, Mixed content, HTTP and HTTPS, Block chain technology for Networks; Web application security - SQL injection, CSRF, XSS and etc.

Introduction to Deep Learning and Motivation.Brief introduction of Artificial Neural Networks (ANN), Perceptrons, Multilayer perceptron (MLP), Back propagation training for MLP,Stochastic gradient descent. Applications to some practical classification problems. Hands on: Demonstration and implementation of Shallow and Deep architecture, introduction to Python, Tensorflow and Keras.

Introduction, Basic Concepts, Familiarize with lab and programming environment, Branching and Loops Designing programming questions for Branching and Loops, Arrays, Functions, Recursion, Designing programming questions for Arrays, Functions and Recursion, Simple Data structures and Algorithms Designing programming questions for Data structures and Algorithms, Testing, Debugging,and How to Evaluation Student Submissions and case studies.

Overview of machine learning, Supervised and unsupervised learning, Artificial Neural Networks, Feedforward Neural networks, Gradient Descent and the back propagation algorithms, Regularization and Optimization. Difference between typical machine learning and deep learning. Convolutional Neural Networks, Autoencoders and Generative Serial Networks, Recurrent Neural Networks, CNN Application to Classification and Detection Problems.

Processing Text using Perl, Use of Regular Expressions, Elements of Morphology, Character N-gram Based Text Mining, Text Classification. Intro and Text Classification, Language Modelling and Sequence Tagging, Vector Space Models of Semantics, Syntactic Processing, Sequence to Sequence Tasks, Dialog Systems, Unification-based NLP and Semantics.