Google Cloud Computing

Exam Preparation PDF

About the Course : 

Those enrolling for the course should ideally:

● Have basic IT knowledge and be interested in learning more about Cloud and ML.

● Have competency in at least one language (such as Python, Java).

● Be familiar with the basics of shell scripting, SQL. 

Summary

Course Status :	Ongoing
Course Type :	Elective
Duration :	8 weeks
Category :	Computer Science and Engineering
Credit Points :	2
Level :	Undergraduate
Start Date :	21 Aug 2023
End Date :	13 Oct 2023
Enrollment Ends :	21 Aug 2023
Exam Registration Ends :	15 Sep 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 0 : Introduction to the course
Week 1 : So, What's the Cloud anyway? Start with a Solid Platform
Week 2 : Use GCP to build your Apps
Week 3 : Where do I store this stuff?
Week 4 : There's an API for that! You can't secure the Cloud right?
Week 5 : It helps to network!
Week 6 : It helps to network (continued)
Week 7 : Let Google keep an eye on things. You have the data, but what are you doing with it?
Week 8 : Let machines do the work

Books and references

https://cloud.google.com/docs/
https://www.qwiklabs.com/

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Introduction To Robotics

Exam Preparation PDF

ABOUT THE COURSE :
This course is a bridge-course for students from various disciplines to get the basic understanding of robotics. The mechanical, electrical, and computer science aspects of robotics is covered in this introductory course.

INTENDED AUDIENCE  : Undergraduate/graduate students interested in robotics

Summary

Course Status :	Ongoing
Course Type :	Core
Duration :	12 weeks
Category :	Design Engineering Robotics
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1: Introduction to robotics- History, growth; Robot applications- Manufacturing industry, defense, rehabilitation, medical etc., Laws of Robotics

Week 2: Robot mechanisms; Kinematics- coordinate transformations, DH parameters

Week 3:  Forward kinematics, Inverse Kinematics

Week 4: Jacobians, Statics, Trajectory Planning

Week 5: Actuators (electrical)- DC motors, BLDC servo motors

Week 6: Sensors , sensor integration

Week 7:  Control – PWM, joint motion control, feedback control

Week 8: Computed torque control

Week 9: Perception, Localisation and mapping

Week 10:Probabilistic robotics, Path planning, BFS; DFS; Dijkstra; A-star; D-star; Voronoi; Potential Field; Hybrid approaches

Week 11: Simultaneous Localization and Mapping

Week 12:Introduction to Reinforcement Learning

Books and references

1. Robert J Schilling, Fundamentals of Robotics, Prentice Hall India, 200
2. John J Craig, Introduction to Robotics, Prentice Hall International, 2005

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Part : 3

 

Programming In Modern C++

Exam Preparation PDF

ABOUT THE COURSE :
There has been a continual debate on which programming language/s to learn, to use. As the latest TIOBE Programming Community Index for August 2021 indicates – C (13%), Python (12%), C++ (7%), Java (10%), and C#(5%) together control nearly half the programming activities worldwide. Further, C Programming Language Family (C, C++, C#, Objective C etc.) dominate more than 25% of activities. Hence, learning C++ is important as one learns about the entire family, about Object-Oriented Programming and gets a solid foundation to also migrate to Java and Python as needed. C++ is the mother of most general purpose of languages. It is multi-paradigm encompassing procedural, object-oriented, generic, and even functional programming. C++ has primarily been the systems language till C++03 which punches efficiency of the code with the efficacy of OOP. Then, why should I learn it if my primary focus is on applications? This is where the recent updates of C++, namely, C++11 and several later offer excellent depths and flexibility for C++ that no language can match. These extensions attempt to alleviate some of the long-standing shortcomings for C++ including porous resource management, error-prone pointer handling, expression semantics, and better readability. The present course builds up on the knowledge of C programming and basic data structure (array, list, stack, queue etc.) to create a strong familiarity with C++98 / C++03. Besides the constructs, syntax and semantics of C++ (over C), we also focus on various idioms of C++ and attempt to go to depth with every C++ feature justifying and illustrating them with several examples and assignment problems. On the way, we illustrate various OOP concepts. The course also covers important advances in C++11 and later released features.

PRE-REQUISITE: Programming & Data Structure (mandatory), Programming in C (optional). Design and Analysis of Algorithms (optional).

INDUSTRY SUPPORT: Programming in C++ is so fundamental that all companies dealing with systems as well as application development (including web, IoT, embedded systems) have a need for the same. These include – Microsoft, Samsung, Xerox, Yahoo, Oracle, Google, IBM, TCS, Infosys, Amazon, Flipkart, etc. This course would help industry developers to be up-to-date with the advances in C++ so that they can remain at the state-of-the-art.

Summary

Course Status :	Ongoing
Course Type :	Core
Duration :	12 weeks
Category :	Computer Science and Engineering
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	28 Oct 2023 IST

Course layout

Week 1: Programming in C++ is Fun.

Week 2: C++ as Better C.

Week 3: OOP in C++.

Week 4: OOP in C++.

Week 5: Inheritance.

Week 6: Polymorphism.

Week 7: Type Casting.

Week 8: Exceptions and Templates.

Week 9: Streams and STL.

Week 10: Modern C++.

Week 11: Lambda and Concurrency.

Week 12: Move, Rvalue and STL Containers.

Books and references

Online Material:

1. C++ reference - C++98 and C++03, C++11, C++14.
2. Overview of the New C++ (C++11/14) by Scott Meyers, 2015.
3. ISO C++ Standards.
4. Presentations used in the Course.

Books:

1. C++ Move Semantics - The Complete Guide by Nicolai M. Josuttis, 2020.
2. C++ Concurrency in Action, 2nd Edition by Anthony Williams, 2019.
3. C++17 - The Complete Guide by Nicolai M. Josuttis, 2020.
4. C++17 In Detail: Learn the Exciting Features of The New C++ Standard! by Bartlomiej Filipek, 2019.
5. Professional C++, 4th Edition by Marc Gregoire, 2018.
6. Functional Programming in C++ by Ivan Čukić, 2018.
7. Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14 by Scott Meyers, 2015.

Exam Preparation PDF

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Part : 3

 

Scientific Computing Using Python

Exam Preparation PDF

ABOUT THE COURSE:

Computation has become an essential tool in science and engineering. In this course, we introduce Python programming language, after which we will cover basics of computational methods. The students will be asked to the solution in Python, which the de facto language now. Topics to be discussed include interpolation, integration, differentiation, ODE and PDE solvers, basic linear algebra, and Monte Carlo techniques. These topics form essential computing tools for computational courses in science and engineering.

INTENDED AUDIENCE: PG students of Science and Engineering (Specially Physics, Mathematics, Mechanical, Aerospace, Computer science and Chemical Engineering). Advance UG students too can take this course.

PREREQUISITES: Basic knowledge of calculus, linear algebra, and ordinary and partial differential equations. Basic knowledge of computation is recommended.

Summary

Course Status :	Ongoing
Course Type :	Elective
Duration :	12 weeks
Category :	Physics
Credit Points :	3
Level :	Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1:
Module 1:About Computers

Module 2:Python variables

Module 3:Python arrays

Week 2:
Module 1:Python Control Structure

Module 2:Python functions

Module 3:Programming style

Week 3:
Module 1:Plotting

Module 2:Data input/output

Module 3:Error analysis and nondimensionalization

Week 4:
Module 1:Lagrange Interpolation

Module 2:Splines

Week 5:
Module 1:Numerical Integration: Newton Cotes

Module 2:Gaussian quadrature

Module 3:Multidimensional and misc integration

Week 6:
Module 1:Differentiation

Module 2:ODE solvers: Euler method

Module 3:ODEs: Implicit schemes

Week 7:
Module 1:ODEs: Higher-order method

Module 2:ODEs: System of eqns, Stiff equations

Module 3:Fourier Transforms

Week 8:
Module 1:Spectral method (PDE solvers): Diffusion equation

Module 2:Spectral method: Wave and Burger eqn solver

Module 3:Spectral: Navier-Stokes eqn solver

Module 4:Spectral: Schrodinger eqn solver

Week 9:
Module 1:Finite Difference (FD) (PDE solvers): Diffusion equation

Module 2:FD method: Wave and Burger eqn solver

Module 3:FD Method: Navier-Stokes eqn solver

Module 4:FD Method: Schrodinger eqn solver

Week 10:
Module 1:Solving Nonlinear Equations (Root finders)

Module 2:Boundary value problems (Shooting method)

Module 3:Eigenvalue solver for diff equatons

Week 11:
Module 1:Lapace equation solvers

Module 2:Lapace equation solvers

Module 3:Poisson equation solvers

Week 12:
Module 1:Linear algebra: Solution of linear equations

Module 2:Linear algebra: Eigenvalues and eigenvectors

Module 3:Intro to Monte Carlo method

Module 4:Summary

Books and references

1. Practical Numerical Computing Using Python : Scientific and Engineering Applications (2021)

2.Mark Newmann: Computational Physics with Python, 2nd Ed.
3.J. M. Stewart: Python for Scientists, Cambridge U. Press (2014)
4.J. H. Ferziger, Numerical Methods for Engineering Applications, John Wiley & Sons (in TB section).
5.M. Lutz, Learning Python 5th Edition, O’Reilly Media (2013)

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Exam Preparation PDF

ABOUT THE COURSE:Objective of this course is to impart knowledge on use of data mining techniques for deriving business intelligence to achieve organizational goals. Use of R (statistical computing software) to build, assess, and compare models based on real datasets and cases with an easy-to-follow learning curve.

INTENDED AUDIENCE:  UG & PG engineering students: all branches, MBA students, Professionals working in or aspiring for Business Analyst, Data Analyst, Data Scientist, and Data Engineer roles

PREREQUISITES:  Business Analytics & Data Mining Modeling Using R

INDUSTRY SUPPORT: Big Data companies, Analytics & Consultancy companies, Companies with Analytics Division

Summary

Course Status :	Ongoing
Course Type :	Elective
Duration :	4 weeks
Category :	Management Studies
Credit Points :	1
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	18 Aug 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	21 Aug 2023
Exam Date :	24 Sep 2023 IST

Course layout

Week 1  : Unsupervised Learning Methods : Association Rules
Week 2  : Unsupervised Learning Methods : Cluster Analysis
Week 3  : Time Series Forecasting: Understanding Time Series and Regression-Based Forecasting Methods
Week 4  : Time Series Forecasting: Smoothing Methods and Conclusion 

Books and references

1. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services (2015)

2. Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner by Shmueli, G., Patel, N. R., & Bruce, P. C. (2010)

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Part : 3

 

German A1

Exam Preparation PDF

ABOUT THE COURSE :

German I is meant to be an introduction to the German language and a basic orientation towards Germany (and to some extent Austria and Switzerland).Learning to understand and articulate oneself in day to day real life situations, and to begin to make sense of Germany as a cultural space are the overall objectives of the course. Serious learners should be able to grasp the basic sentence structure and build a goodfoundational vocabulary through this course.

INTENDED AUDIENCE: Anyone interested in learning elementary German

 

INDUSTRY SUPPORT:  Companies / Organisations / Individuals having business / work with Germany, Austria and/or Switzerland

Summary

Course Status :	Ongoing
Course Type :	Elective
Duration :	12 weeks
Category :	Humanities and Social Sciences
Credit Points :	3
Level :	Undergraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1:  Themes: Introducing oneself and others; Grammar: W questions, personal pronouns, simple sentence, verb conjugation

Week 2:  Themes: hobbies, the week, numbers, the alphabet, months, seasons /Grammar : articles , plural, the verbs to have and to be

Week 3:  Theme: In the city / naming places and buildings, means of transport, basic directions / Grammar : definite and indefinite articles; negation - kein and nicht; imperative  

Week 4:  Themes: food, drink, family / groceries and meals / Grammar : the accusative

Week 5:  Theme: Everyday life, telling time, making appointments / Grammar :prepositions am, um, von..bis; modal verbs, possessive articles

Week 6:  Leisure activity, celebrations / Grammar: separable verbs, the accusative, past tense of to have and to be

Week 7:  Contacts, writing letters / Grammar: dative

Week 8:  My apartment, rooms, furniture, colours / Grammar: changing prepositions

Week 9:  Professions / Grammar : perfect tense

Week 10: Clothes / Grammar: perfect tense and dative

Week 11: Health and the body / Grammar: the imperative and modal verbs

Week 12: Holiday and weather

Books and references

Prescribed Textbook: NETZWERK Deutsch als Fremdsprache A1(Goyal, New Delhi, 2015)

Other recommended books:

Schulz-Griesbach: Deutsch als Fremdsprache. Grundstufe in einem Band (for Grammar)

Web Resources:

FACTS ABOUT GERMANY https://www.tatsachen-ueber-deutschland.de/en

ONLINE GERMAN-ENGLISH DICTIONARY www.leo.org

PRACTICE MATERIAL

https://www.goethe.de/en/spr/kup/prf/prf/sd1/ueb.html

http://www.deutschkurse-

passau.de/JM/images/stories/SKRIPTEN/a1_skript_gr.pdf

https://www.schubert-

verlag.de/aufgaben/arbeitsblaetter_a1_z/a1_arbeitsblaetter_index_z.htm

Exam Preparation PDF

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History Of English Language And Literature

Exam Preparation PDF

ABOUT THE COURSE :

This course is a chronological survey of the major forces and voices that have contributed to the development of an English literary tradition. It intends to cover the literary ground from the Old English Period till the mid twentieth century focusing on the emergence, evolution and progress of English language and literature through different ages and periods. The course will showcase major literary moments, movements and events in the context of the social, political. religious and economic changes that shaped England and its history from the 5th century BC on wards. The objective of the course is to enable a critical understanding of the intellectual history of England and to equip the learners to analyse literary products within particular socio-historical contexts. 

INTENDED AUDIENCE: Any Interested Learners

PRE-REQUISITES: No formal pre-requisites.However, a familiarity with and an interest in English literary studies would be much appreciated.Industry that will recognize this course.The course is best suited for the academic fraternity.  

Summary

Course Status :	Ongoing
Course Type :	Core
Duration :	12 weeks
Category :	Humanities and Social Sciences English Studies
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1: Introduction and Old English Period

Week 2: Middle English Period and Renaissance

Week 3: English Renaissance and Elizabethan Period

Week 4: The Age of Shakespeare

Week 5: The Restoration Age to Enlightenment

Week 6: Augustan Age

Week 7: The Romantic Age

Week 8: The Age of Wordsworth and Romantic poetry

Week 9: The Victorian Age

Week 10: The Rise of the Novel

Week 11: The Age of Modernism

Week 12: The Age of Postmodernism

Books and references

1. An Outline History of English Literature – William Henry Hudson,The Cambridge
2. Companion to Old English Literature – ed. Malcolm Godden and Michael Lapidge,
3. History of English Literature, Fifth edition – Edward Albert,The Oxford
4. Illustrated History of English Literature – Pat Rogers,
5. English Social History: A Survey of Six Centuries – Chaucer to Queen Victoria – G M Trevelyan,
6. An Outline History of the English Language – Frederick T Wood,
7. The Oxford English Literary History, Vol 12 / 1960-2000 – Randall Stevenson,
8. A Critical History of English Literature (4 volumes) – David Daiches,
9. The Routledge History of Literature in English, 2nd edition

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Machine Learning And Deep Learning - Fundamentals And Applications

Exam Preparation PDF

ABOUT THE COURSE:

In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. We will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. On completion of the course students will acquire the knowledge of applying Machine and Deep Learning techniques to solve various real-life problems.

INTENDED AUDIENCE: UG, PG and PhD students and industry professionals who want to work in Machine and Deep Learning.

PREREQUISITES: Knowledge of Linear Algebra, Probability and Random Process, PDE will be helpful.

INDUSTRY SUPPORT: This is a very important course for industry professionals.

Course Status :	Ongoing
Course Type :	Core
Duration :	12 weeks
Category :	Computer Science and Engineering Electrical, Electronics and Communications Engineering Communication and Signal Processing
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1: Introduction
Introduction to ML, Performance Measures, Bias-Variance Trade off, Linear Regression.
Week 2: Bayes Decision Theory
Bayes Decision Theory, Normal Density and Discriminant Function, Bayes Decision Theory - Binary Features, Bayesian Belief Network
Week 3: Parametric and Non- Parametric Density Estimation
Parametric and Non- Parametric Density Estimation – ML and Bayesian Estimation, Parzen Window and KNN
Week 4:Perceptron Criteria and Discriminative Models
Perceptron Criteria, Discriminative models, Support Vector Machines (SVM)
Week 5: Logistic Regression, Decision Trees and Hidden Markov Model
Logistic Regression, Decision trees, Hidden Markov Model (HMM)
Week 6: Ensemble methods
Ensemble methods: Ensemble strategies, boosting and bagging, Random Forest
Week 7: Dimensionality Problem
Dimensionality Problem, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
Week 8: Mixture Model and Clustering
Concept of mixture model, Gaussian mixture model, Expectation Maximization Algorithm, K- means clustering.
Week 9: Clustering
Fuzzy K-means clustering, Hierarchical Agglomerative Clustering, Mean-shift clustering.
Week 10: Neural Network
Neural network: Perceptron, multilayer network, backpropagation, RBF Neural Network, Applications
Week 11: Introduction to Deep Neural Networks
Introduction to Deep Learning, Convolutional Neural Networks (CNN),
Vanishing and Exploding Gradients in Deep Neural Networks, LeNet - 5, AlexNet, VGGNet, GoogleNet, and ResNet.
Week 12: Recent Trends in Deep Learning
Generative Adversarial Networks (GAN), Auto Encoders and Relation to PCA, Recurrent Neural Networks, U-Net, Applications and Case studies.

Books and references

1. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India) 2015.

2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.

3. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),Springer, 2006.

4. M.K. Bhuyan, Computer Vision and Image Processing: Fundamentals and Applications, published by CRC press, USA, 2019.

5. S. O. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson Education (India), 2016.

6. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016

7. Michael A. Nielsen, Neural Networks and Deep Learning , Determination Press, 2015

8. Yoshua Bengio, Learning Deep Architectures for AI, now Publishers Inc., 2009

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