Assignments using Lex and Yaac
Compilers: Principles, Techniques and Tools, V. Aho, R. Sethi and J. Ullman. PearsonEducation
Lex &Yacc, Levine R. John, Tony Mason and Doug Brown, O'Reilly
The Design and Evolution of C++, Bjarne Stroustrup.
Compiler Design, Raghavan, TMH Pub.
Compiler Construction: Principles and Practice, Louden, Cengage Learning
Compiler Design in C, A. C. Holub. Prentice-Hall Inc., 1993.
Writing compiler & Interpreters, Mak, Willey Pub.
Unit I - Introduction: State of the art applications in Atari, Alpha Go, relation to other problems in artificial intelligence. Markov Decision Processes (model based): Formulation, Value Iteration (VI), Policy Iteration (PI), Linear Programming (LP).
Unit II -Approximate Dynamic Programming (approximate model based): curse-of- dimensionality, representations, Approximate value iteration, approximate policy iteration, approximate linear program, approximation and convergence guarantees.
Unit III -Stochastic Approximation: Single and multi-timescale stochastic approximation, introduction to ordinary differential equation based convergence results. Value function learning (approximate model-free): Temporal difference (TD learning, TD(0), TD(lambda), Q-learning, State-Action-Reward-State Algorithm (SARSA) , TD with function approximation, on/off-policy learning, gradient temporal difference learning.
Unit IV - Actor-Critic: Policy gradient, Natural Actor-Critic, Deep RL.
Unit V -Exploration vs Exploitation: Upper Confidence Bound (UCB), Upper Confidence Reinforcement Learning (UCRL).
Text Books :
Richard S. Sutton and Andrew G. Barto, Introduction to Reinforcement Learning, 2nd Edition, MIT Press. 2017. ISBN-13 978-0262039246.
Dimitri Bertsekas and John G. Tsitsiklis, Neuro Dynamic Programming, Athena Scientific. 1996. ISBN-13: 978-1886529106.
References :
V. S. Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint, Hindustan Book Agency, 2009. ISBN-13: 978-0521515924
Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville. MIT Press. 2016.ISBN-13: 978-0262035613.
Course outcomes :
The student should be able to -
model a control task in the framework of MDPs.
Identify the model based from the model free methods.
Identify stability/convergence and approximation properties of RL algorithms.
Use deep learning methods to RL problems in practice.
Learn activities involved in IT projects management.
Apply agile process to project management.
Plan application development using Scrum.
Develop abilities to use DevOps in projects.
Develop understanding of Containers use in projects.
Mike Cohn, “Succeeding with Agile: Software Development Using Scrum”, Addison Wesley, 2009
Pearson, Robert C. Martin, Juli, James Shore, “The Art Of Agile Development”, O'Reilly, 2013
John Hunt, “Agile Software Construction”, 1st Edition, Springer,2005
Somerville, “Software Engineering”, 10th edition (Chapter 3, Chapters 22 to 26), Pearson, 2017
Deepak Gaikwad, Viral Thakkar, “DevOps Tools from Practitioner's Viewpoint”, Wiley, 2019
James Turnbill, “The Docker Book”, 2019
Roman Pichler, “Agile Product Management with Scrum”.
Ken Schwaber, “Agile Project Management with Scrum” (Microsoft Professional)
Andrew Stellman, Jenifer Greene, “Head First Agile”, Oreilly, 2017
Peggy Gregory, Casper Lassenius, Xiaofeng Wang Philippe Kruchten (Eds.), “Agile Processes in Software Engineering and Extreme Programming”, 22nd International Conference on Agile Software Development, XP 2021 Virtual Event, June 14–18, 2021, Proceedings, Springer
Joseph Phillips, IT Project Management: On Track from Start to Finish, 3rd Edition, McGraw-Hill, 2010
Clinton Keith, “Agile Game Development”, Addison Wesley, 2010
Scott M Graffius, “Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions”, CreateSpace, 2016
Wendy L. Martinez and Angel R, “Martinez Computational Statistics,” Chapman & Hall/CRC, 2002.
Ian H. Witten, “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, Morgan Kaufmann, 2000.
Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, 2001.
K. P. Soman, V. Ajay and Diwakar Shyam, “Insight into Data Mining: Theory and Practice”, Prentice Hall India, 2005.
New Scheme Based On AICTE Flexible Curricula
Review of Linear Algebra for machine learning; Introduction and motivation for machine learning;
Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization, Bias variance trade-off.
Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function – Perceptron algorithm, Probabilistic discriminative model - Logistic regression, Probabilistic generative model – Naive Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random Forests
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning - bagging, boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization.
Multilayer perceptron, activation functions, network training – gradient descent optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep networks –Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch normalization, regularization, dropout.
Guidelines for machine learning experiments, Cross Validation (CV) and resampling – K-fold CV, bootstrapping, measuring classifier performance, assessing a single classification algorithm and comparing two classification algorithms – t test, McNemar’s test, K-fold CV paired t test
At the end of this course, the students will be able to:
CO1: Explain the basic concepts of machine learning. CO2 : Construct supervised learning models.
CO3 : Construct unsupervised learning algorithms. CO4: Evaluate and compare different models
Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.
Stephen Marsland, “Machine Learning: An Algorithmic Perspective, “Second Edition”, CRC Press,
Melanie Swan, “Block Chain: Blueprint for a New Economy”, O’Reilly, 2015
Josh Thompsons, “Block Chain: The Block Chain for Beginners- Guide to Block chain Technology and Leveraging Block Chain Programming”
Daniel Drescher, “Block Chain Basics”, Apress; 1stedition, 2017
Anshul Kaushik, “Block Chain and Crypto Currencies”, Khanna Publishing House, Delhi.
Imran Bashir, “Mastering Block Chain: Distributed Ledger Technology, Decentralization and Smart Contracts Explained”, Packt Publishing
Ritesh Modi, “Solidity Programming Essentials: A Beginner’s Guide to Build Smart Contracts for Ethereum and Block Chain”, Packt Publishing
Salman Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, Anthony O’Dowd, Venkatraman Ramakrishna, “Hands-On Block Chain with Hyper ledger: Building Decentralized Applications with Hyperledger Fabric and Composer”, Import, 2018
Forecasting Process, Data for forecasting, Resources for forecasting.
Autoregressive Moving Average (ARMA) Models – Stationarityand Invertibility of ARMA Models - Checking for Stationarity usingVariogram- Detecting Nonstationarity - Autoregressive Integrated MovingAverage (ARIMA) Models - Forecasting using ARIMA - Seasonal Data -Seasonal ARIMA Models Forecasting using Seasonal ARIMA ModelsIntroduction - Finding the “BEST” Model -Example: Internet Users DataModel Selection Criteria - Impulse Response Function to Study theDifferences in Models Comparing Impulse Response Functions forCompeting Models .
Series Models and Forecasting, Multivariate StationaryProcess, Vector ARIMA Models, Vector AR (VAR) Models, NeuralNetworks and Forecasting Spectral Analysis, Bayesian Methods inForecasting.
Time Series Data Cleaning
Loading and Handling Times series data
Preprocessing Techniques
How to Check Stationarity of a Time Series.
How to make a Time Series Stationary?
Estimating & Eliminating Trend.
Aggregation
Smoothing
Polynomial Fitting
Eliminating Trend and Seasonality
Differencing
Decomposition
Moving Average time analysis data.
Smoothing the Time analysis Data.
Check out the Time series Linear and non-linear trends.
Create a modelling.
Modelling time series
Moving average
Exponential smoothing
ARIMA
Seasonal autoregressive integrated moving average model (SARIMA)
Dependence Techniques
Multivariate Analysis of Variance and Covariance
Canonical Correlation Analysis
Structural Equation Modeling
Inter-Dependence Techniques
Factor Analysis
Cluster Analysis
Vijay Madisetti, Arshdeep Bahga, “Ïnternet of Things, A Hands on Approach”, University Press
Dr. SRN Reddy, Rachit Thukral and Manasi Mishra, “Introduction to Internet of Things: A practical Approach”,
ETI Labs
Pethuru Raj and Anupama C. Raman, “The Internet of Things: Enabling Technologies, Platforms, and Use
Cases”, CRC Press
Jeeva Jose, “Internet of Things”, Khanna Publishing House, Delhi
Adrian McEwen, “Designing the Internet of Things”, Wiley
Raj Kamal, “Internet of Things: Architecture and Design”, McGraw Hill
Cuno Pfister, “Getting Started with the Internet of Things”, O Reilly Media
After the completion of this course, the students will be able to:
Understand Internet of Things and its hardware and software components
Interface I/O devices, sensors & communication modules
Analyze data from various sources in real-time and take necessary actions in an intelligent fashion
Remotely monitor data and control devices
Develop real life IoT based projects
Course Objectives: The objective of this course is to impart necessary knowledge to the learner so that he/she can develop and implement algorithm and write programs using these algorithm
Michael A. Nielsen, “Quantum Computation and Quantum Information”, Cambridge University Press.
David McMahon, “Quantum Computing Explained”, Wiley
After the completion of this course, the students will be able to:
Understand major concepts in Quantum Computing
Explain the working of a Quantum Computing program, its architecture and program model
Develop quantum logic gate circuits
Develop quantum algorithm
Program quantum algorithm on major toolkits.
Assignments using Lex and Yaac
Compilers: Principles, Techniques and Tools, V. Aho, R. Sethi and J. Ullman. PearsonEducation
Lex &Yacc, Levine R. John, Tony Mason and Doug Brown, O'Reilly
The Design and Evolution of C++, Bjarne Stroustrup.
Compiler Design, Raghavan, TMH Pub.
Compiler Construction: Principles and Practice, Louden, Cengage Learning
Compiler Design in C, A. C. Holub. Prentice-Hall Inc., 1993.
Writing compiler & Interpreters, Mak, Willey Pub.
Unit I - Introduction: State of the art applications in Atari, Alpha Go, relation to other problems in artificial intelligence. Markov Decision Processes (model based): Formulation, Value Iteration (VI), Policy Iteration (PI), Linear Programming (LP).
Unit II -Approximate Dynamic Programming (approximate model based): curse-of- dimensionality, representations, Approximate value iteration, approximate policy iteration, approximate linear program, approximation and convergence guarantees.
Unit III -Stochastic Approximation: Single and multi-timescale stochastic approximation, introduction to ordinary differential equation based convergence results. Value function learning (approximate model-free): Temporal difference (TD learning, TD(0), TD(lambda), Q-learning, State-Action-Reward-State Algorithm (SARSA) , TD with function approximation, on/off-policy learning, gradient temporal difference learning.
Unit IV - Actor-Critic: Policy gradient, Natural Actor-Critic, Deep RL.
Unit V -Exploration vs Exploitation: Upper Confidence Bound (UCB), Upper Confidence Reinforcement Learning (UCRL).
Text Books :
Richard S. Sutton and Andrew G. Barto, Introduction to Reinforcement Learning, 2nd Edition, MIT Press. 2017. ISBN-13 978-0262039246.
Dimitri Bertsekas and John G. Tsitsiklis, Neuro Dynamic Programming, Athena Scientific. 1996. ISBN-13: 978-1886529106.
References :
V. S. Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint, Hindustan Book Agency, 2009. ISBN-13: 978-0521515924
Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville. MIT Press. 2016.ISBN-13: 978-0262035613.
Course outcomes :
The student should be able to -
model a control task in the framework of MDPs.
Identify the model based from the model free methods.
Identify stability/convergence and approximation properties of RL algorithms.
Use deep learning methods to RL problems in practice.
Learn activities involved in IT projects management.
Apply agile process to project management.
Plan application development using Scrum.
Develop abilities to use DevOps in projects.
Develop understanding of Containers use in projects.
Mike Cohn, “Succeeding with Agile: Software Development Using Scrum”, Addison Wesley, 2009
Pearson, Robert C. Martin, Juli, James Shore, “The Art Of Agile Development”, O'Reilly, 2013
John Hunt, “Agile Software Construction”, 1st Edition, Springer,2005
Somerville, “Software Engineering”, 10th edition (Chapter 3, Chapters 22 to 26), Pearson, 2017
Deepak Gaikwad, Viral Thakkar, “DevOps Tools from Practitioner's Viewpoint”, Wiley, 2019
James Turnbill, “The Docker Book”, 2019
Roman Pichler, “Agile Product Management with Scrum”.
Ken Schwaber, “Agile Project Management with Scrum” (Microsoft Professional)
Andrew Stellman, Jenifer Greene, “Head First Agile”, Oreilly, 2017
Peggy Gregory, Casper Lassenius, Xiaofeng Wang Philippe Kruchten (Eds.), “Agile Processes in Software Engineering and Extreme Programming”, 22nd International Conference on Agile Software Development, XP 2021 Virtual Event, June 14–18, 2021, Proceedings, Springer
Joseph Phillips, IT Project Management: On Track from Start to Finish, 3rd Edition, McGraw-Hill, 2010
Clinton Keith, “Agile Game Development”, Addison Wesley, 2010
Scott M Graffius, “Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions”, CreateSpace, 2016
Wendy L. Martinez and Angel R, “Martinez Computational Statistics,” Chapman & Hall/CRC, 2002.
Ian H. Witten, “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, Morgan Kaufmann, 2000.
Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, 2001.
K. P. Soman, V. Ajay and Diwakar Shyam, “Insight into Data Mining: Theory and Practice”, Prentice Hall India, 2005.
New Scheme Based On AICTE Flexible Curricula
Review of Linear Algebra for machine learning; Introduction and motivation for machine learning;
Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization, Bias variance trade-off.
Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function – Perceptron algorithm, Probabilistic discriminative model - Logistic regression, Probabilistic generative model – Naive Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random Forests
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning - bagging, boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization.
Multilayer perceptron, activation functions, network training – gradient descent optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep networks –Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch normalization, regularization, dropout.
Guidelines for machine learning experiments, Cross Validation (CV) and resampling – K-fold CV, bootstrapping, measuring classifier performance, assessing a single classification algorithm and comparing two classification algorithms – t test, McNemar’s test, K-fold CV paired t test
At the end of this course, the students will be able to:
CO1: Explain the basic concepts of machine learning. CO2 : Construct supervised learning models.
CO3 : Construct unsupervised learning algorithms. CO4: Evaluate and compare different models
Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.
Stephen Marsland, “Machine Learning: An Algorithmic Perspective, “Second Edition”, CRC Press,
Melanie Swan, “Block Chain: Blueprint for a New Economy”, O’Reilly, 2015
Josh Thompsons, “Block Chain: The Block Chain for Beginners- Guide to Block chain Technology and Leveraging Block Chain Programming”
Daniel Drescher, “Block Chain Basics”, Apress; 1stedition, 2017
Anshul Kaushik, “Block Chain and Crypto Currencies”, Khanna Publishing House, Delhi.
Imran Bashir, “Mastering Block Chain: Distributed Ledger Technology, Decentralization and Smart Contracts Explained”, Packt Publishing
Ritesh Modi, “Solidity Programming Essentials: A Beginner’s Guide to Build Smart Contracts for Ethereum and Block Chain”, Packt Publishing
Salman Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, Anthony O’Dowd, Venkatraman Ramakrishna, “Hands-On Block Chain with Hyper ledger: Building Decentralized Applications with Hyperledger Fabric and Composer”, Import, 2018
Forecasting Process, Data for forecasting, Resources for forecasting.
Autoregressive Moving Average (ARMA) Models – Stationarityand Invertibility of ARMA Models - Checking for Stationarity usingVariogram- Detecting Nonstationarity - Autoregressive Integrated MovingAverage (ARIMA) Models - Forecasting using ARIMA - Seasonal Data -Seasonal ARIMA Models Forecasting using Seasonal ARIMA ModelsIntroduction - Finding the “BEST” Model -Example: Internet Users DataModel Selection Criteria - Impulse Response Function to Study theDifferences in Models Comparing Impulse Response Functions forCompeting Models .
Series Models and Forecasting, Multivariate StationaryProcess, Vector ARIMA Models, Vector AR (VAR) Models, NeuralNetworks and Forecasting Spectral Analysis, Bayesian Methods inForecasting.
Time Series Data Cleaning
Loading and Handling Times series data
Preprocessing Techniques
How to Check Stationarity of a Time Series.
How to make a Time Series Stationary?
Estimating & Eliminating Trend.
Aggregation
Smoothing
Polynomial Fitting
Eliminating Trend and Seasonality
Differencing
Decomposition
Moving Average time analysis data.
Smoothing the Time analysis Data.
Check out the Time series Linear and non-linear trends.
Create a modelling.
Modelling time series
Moving average
Exponential smoothing
ARIMA
Seasonal autoregressive integrated moving average model (SARIMA)
Dependence Techniques
Multivariate Analysis of Variance and Covariance
Canonical Correlation Analysis
Structural Equation Modeling
Inter-Dependence Techniques
Factor Analysis
Cluster Analysis
Vijay Madisetti, Arshdeep Bahga, “Ïnternet of Things, A Hands on Approach”, University Press
Dr. SRN Reddy, Rachit Thukral and Manasi Mishra, “Introduction to Internet of Things: A practical Approach”,
ETI Labs
Pethuru Raj and Anupama C. Raman, “The Internet of Things: Enabling Technologies, Platforms, and Use
Cases”, CRC Press
Jeeva Jose, “Internet of Things”, Khanna Publishing House, Delhi
Adrian McEwen, “Designing the Internet of Things”, Wiley
Raj Kamal, “Internet of Things: Architecture and Design”, McGraw Hill
Cuno Pfister, “Getting Started with the Internet of Things”, O Reilly Media
After the completion of this course, the students will be able to:
Understand Internet of Things and its hardware and software components
Interface I/O devices, sensors & communication modules
Analyze data from various sources in real-time and take necessary actions in an intelligent fashion
Remotely monitor data and control devices
Develop real life IoT based projects
Course Objectives: The objective of this course is to impart necessary knowledge to the learner so that he/she can develop and implement algorithm and write programs using these algorithm
Michael A. Nielsen, “Quantum Computation and Quantum Information”, Cambridge University Press.
David McMahon, “Quantum Computing Explained”, Wiley
After the completion of this course, the students will be able to:
Understand major concepts in Quantum Computing
Explain the working of a Quantum Computing program, its architecture and program model
Develop quantum logic gate circuits
Develop quantum algorithm
Program quantum algorithm on major toolkits.