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For detailed syllabus of all the other subjects of IT 6th Sem, 2021 curriculum do visit IT 6th Sem subject syllabuses for 2021 regulation. For Information Technology 6th Sem semesters scheme and subjects, refer to IT 6th Sem 2021 regulation scheme. The scheme details of Professional Elective-VII for IT 6th Sem is as follows.

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For updated syllabus of Information Technology (IT) 2021, kindly visit IT updated syllabus.

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For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of cognitive science is as follows.

Course Objectives:

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Unit I

PHILOSOPHY, PSYCHOLOGY AND NEUROSCIENCE 6 Philosophy: Mental-physical Relation – From Materialism to Mental Science – Logic and the Sciences of the Mind – Psychology: Place of Psychology within Cognitive Science – Science of Information Processing -Cognitive Neuroscience – Perception – Decision – Learning and Memory – Language Understanding and Processing.

Unit II

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Unit III

PROBABILISTIC PROGRAMMING LANGUAGE 6 WebPPL Language – Syntax – Using Javascript Libraries – Manipulating probability types and distributions – Finding Inference – Exploring random computation – Coroutines: Functions that receive continuations -Enumeration

Unit IV

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Unit V

LEARNING MODELS OF COGNITION 6 Learning as Conditional Inference – Learning with a Language of Thought – Hierarchical Models-Learning (Deep) Continuous Functions – Mixture Models.

Practical Exercises

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Course Outcomes:

At the end of this course, the students will be able to:

1. Understand the underlying theory behind cognition.
2. Connect to the cognition elements computationally.
3. Implement mathematical functions through WebPPL.
4. Develop applications using cognitive inference model.
5. Develop applications using cognitive learning model.

Text Books:

1. Vijay V Raghavan,Venkat N.Gudivada, VenuGovindaraju, C.R. Rao, Cognitive Computing: Theory and Applications: (Handbook of Statistics 35), Elsevier publications, 2016
2. Judith Hurwitz, Marcia Kaufman, Adrian Bowles, Cognitive Computing and Big Data Analytics, Wiley Publications, 2015
3. Robert A. Wilson, Frank C. Keil, The MIT Encyclopedia of the Cognitive Sciences,The MIT Press, 1999.
4. Jose Luis Bermudez, Cognitive Science -An Introduction to the Science of the Mind, Cambridge University Press 2020

Reference Books:

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For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of ethics and ai is as follows.

Course Objectives:

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Unit I

INTRODUCTION 6 Definition of morality and ethics in AI-Impact on society-Impact on human psychology-Impact on the legal system-Impact on the environment and the planet-Impact on trust

Unit II

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Unit III

AI STANDARDS AND REGULATION 6 Model Process for Addressing Ethical Concerns During System Design – Transparency of Autonomous Systems-Data Privacy Process- Algorithmic Bias Considerations Ontological Standard for Ethically Driven Robotics and Automation Systems

Unit IV

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Unit V

AI AND ETHICS- CHALLENGES AND OPPORTUNITIES 6 Challenges – Opportunities- ethical issues in artificial intelligence- Societal Issues Concerning the Application of Artificial Intelligence in Medicine- decision-making role in industries-National and International Strategies on AI.

Course Outcomes:

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Practical Exercises

1. Recent case study of ethical initiatives in healthcare, autonomous vehicles and defense
2. Exploratory data analysis on a 2 variable linear regression model
3. Experiment the regression model without a bias and with bias
4. Classification of a dataset from UCI repository using a perceptron with and without bias
5. Case study on ontology where ethics is at stake
6. Identification on optimization in AI affecting ethics

Text Books:

1. y. Eleanor Bird, Jasmin Fox-Skelly, Nicola Jenner, Ruth Larbey, Emma Weitkamp and Alan Winfield ,The ethics of artificial intelligence: Issues and initiatives, EPRS | European Parliamentary Research Service Scientific Foresight Unit (STOA) PE 634.452 – March 2020
2. Patrick Lin, Keith Abney, George A Bekey, Robot Ethics: The Ethical and Social Implications of Robotics, The MIT Press- January 2014.

Reference Books:

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Web Link:

1. https://sci-hub.mksa.top/10.1007/978-3-540-30301-5_65
2. https://www.scu.edu/ethics/all-about-ethics/artificial-intelligence-and-ethics-sixteen-challenges-and-opportunities/
3. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/
4. https://sci-hub.mksa.top/10.1159/000492428

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of game theory is as follows.

Course Objectives:

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Unit I

INTRODUCTION 6 Introduction Making rational choices: basics of Games strategy preferences payoffs Mathematical basics Game theory Rational Choice Basic solution concepts-non-cooperative versus cooperative games Basic computational issues finding equilibria and learning in games- Typical application areas for game theory (e.g. Google’s sponsored search, eBay auctions, electricity trading markets).

Unit II

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Unit III

GAMES WITH IMPERFECT INFORMATION 6 Games with Imperfect Information Bayesian Games Motivational Examples General Definitions Information aspects Illustrations Extensive Games with Imperfect Information Strategies Nash Equilibrium Repeated Games The Prisoner’s Dilemma Bargaining

Unit IV

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Unit V

MECHANISM DESIGN 6 Aggregating Preferences Social Choice Formal Model Voting Existence of social functions Ranking systems Protocols for Strategic Agents: Mechanism Design Mechanism design with unrestricted preferences

Course Outcomes:

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Laboratory Exercises:

1. Prisoners dilemma
2. Pure Strategy Nash Equilibrium
3. Extensive Form – Graphs and Trees, Game Trees
4. Strategic Form – Elimination of dominant strategy
5. Minimax theorem, minimax strategies
6. Perfect information games: trees, players assigned to nodes, payoffs, backward Induction, subgame perfect equilibrium,
7. imperfect-information games – Mixed Strategy Nash Equilibrium – Finding mixed-strategy Nash equilibria for zero sum games, mixed versus behavioral strategies.
8. Repeated Games
9. Bayesian Nash equilibrium

Text Books:

1. M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2012.
2. M. Machler, E. Solan, S. Zamir, Game Theory, Cambridge University Press, 2013.
3. N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, Algorithmic Game Theory. Cambridge University Press, 2007.
4. A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc, 2004.
5. YoavShoham, Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press 2008.
6. Zhu Han, DusitNiyato, WalidSaad, TamerBasar and Are Hjorungnes, Game Theory in Wireless and Communication Networks, Cambridge University Press, 2012.
7. Y.Narahari, Game Theory and Mechanism Design, IISC Press, World Scientific.
8. William Spaniel, Game Theory 101: The Complete Textbook, CreateSpace Independent Publishing, 2011.

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of text and speech analysis is as follows.

Course Objectives:

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Unit I

NATURAL LANGUAGE BASICS 6 Foundations of natural language processing – Language Syntax and Structure- Text Preprocessing and Wrangling – Text tokenization – Stemming – Lemmatization – Removing stop-words – Feature Engineering for Text representation – Bag of Words model- Bag of N-Grams model – TF-IDF model

Suggested Activities

• Flipped classroom on NLP
• Implementation of Text Preprocessing using NLTK
• Implementation of TF-IDF models

Suggested Evaluation Methods

• Quiz on NLP Basics
• Demonstration of Programs

Unit II

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Unit III

QUESTION ANSWERING AND DIALOGUE SYSTEMS 9 Information retrieval – IR-based question answering – knowledge-based question answering -language models for QA – classic QA models – chatbots – Design of dialogue systems -evaluating dialogue systems

Suggested Activities:

• Flipped classroom on language models for QA
• Developing a knowledge-based question-answering system
• Classic QA model development

Suggested Evaluation Methods

• Assignment on the above topics
• Quiz on knowledge-based question answering system
• Development of simple chatbots

Unit IV

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Unit V

AUTOMATIC SPEECH RECOGNITION 6 Speech recognition: Acoustic modelling – Feature Extraction – HMM, HMM-DNN systems

Suggested Activities:

• Flipped classroom on Speech recognition.
• Exploring Feature extraction

Suggested Evaluation Methods

• Assignment on the above topics
• Quiz on acoustic modelling

Practical Exercises

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Course Outcomes:

On completion of the course, the students will be able to

1. Explain existing and emerging deep learning architectures for text and speech processing
2. Apply deep learning techniques for NLP tasks, language modelling and machine translation
3. Explain coreference and coherence for text processing
4. Build question-answering systems, chatbots and dialogue systems
5. Apply deep learning models for building speech recognition and text-to-speech systems

Text Books:

1. Daniel Jurafsky and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Third Edition, 2022.

Reference Books:

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For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of optimization techniques is as follows.

Course Objectives:

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Unit I

LINEAR MODELS 6 Introduction of Operations Research – mathematical formulation of LPP- Graphical Methods to solve LPP- Simplex Method- Two-Phase method

Unit II

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Unit III

PROJECT SCHEDULING 6 Project network -Diagram representation – Floats – Critical path method (CPM) – PERT- Cost considerations in PERT and CPM.

Unit IV

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Unit V

QUEUING MODELS 6 Introduction, Queuing Theory, Operating characteristics of a Queuing system, Constituents of a Queuing system, Service facility, Queue discipline, Single channel models, multiple service channels.

Practicals

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Course Outcomes:

On successful completion of this course, the student will able to

1. Formulate and solve linear programming problems (LPP)
2. Evaluate Integer Programming Problems, Transportation and Assignment Problems.
3. Obtain a solution to network problems using CPM and PERT techniques.
4. Able to optimize the function subject to the constraints.
5. Identify and solve problems under Markovian queuing models

Text Books:

1. Hamdy A Taha, Operations Research: An Introduction, Pearson, 10th Edition, 2017.

Reference Books:

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of neural networks and deep learning is as follows.

Course Objectives:

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Unit I

INTRODUCTION 6 Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of ANNs-Supervised Learning Network.

Unit II

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Unit III

THIRD-GENERATION NEURAL NETWORKS 6 Spiking Neural Networks-Convolutional Neural Networks-Deep Learning Neural Networks-Extreme Learning Machine Model-Convolutional Neural Networks: The Convolution Operation – Motivation -Pooling – Variants of the basic Convolution Function – Structured Outputs – Data Types – Efficient Convolution Algorithms – Neuroscientific Basis – Applications: Computer Vision, Image Generation, Image Compression.

Unit IV

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Unit V

RECURRENT NEURAL NETWORKS 6 Recurrent Neural Networks: Introduction – Recursive Neural Networks – Bidirectional RNNs – Deep Recurrent Networks – Applications: Image Generation, Image Compression, Natural Language Processing. Complete Auto encoder, Regularized Autoencoder, Stochastic Encoders and Decoders, Contractive Encoders.

Lab Experiments:

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Additional Experiments:

1. Train a Deep learning model to classify a given image using pre trained model
2. Recommendation system from sales data using Deep Learning
3. Implement Object Detection using CNN
4. Implement any simple Reinforcement Algorithm for an NLP problem

Course Outcomes:

At the end of this course, the students will be able to:

1. Apply Convolution Neural Network for image processing.
2. Understand the basics of associative memory and unsupervised learning networks.
3. Apply CNN and its variants for suitable applications.
4. Analyze the key computations underlying deep learning and use them to build and train deep neural networks for various tasks.
5. Apply autoencoders and generative models for suitable applications.

Text Books:

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Reference Books:

1. Aurelien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, Oreilly, 2018.
2. Josh Patterson, Adam Gibson, Deep Learning: A Practitioners Approach, OReilly Media, 2017.
3. Charu C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer International Publishing, 1st Edition, 2018.
4. Learn Keras for Deep Neural Networks, Jojo Moolayil, Apress,2018
5. Deep Learning Projects Using TensorFlow 2, Vinita Silaparasetty, Apress, 2020
6. Deep Learning with Python, FRANQOIS CHOLLET, MANNING SHELTER ISLAND,2017.
7. S Rajasekaran, G A Vijayalakshmi Pai, Neural Networks, FuzzyLogic and Genetic Algorithm, Synthesis and Applications, PHI Learning, 2017.
8. Pro Deep Learning with TensorFlow, Santanu Pattanayak, Apress,2017
9. James A Freeman, David M S Kapura, Neural Networks Algorithms, Applications, and Programming Techniques, Addison Wesley, 2003.

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of soft computing is as follows.

Course Objectives:

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Unit I

INTRODUCTION TO SOFT COMPUTING AND FUZZY LOGIC 6 Introduction – Fuzzy Logic – Fuzzy Sets, Fuzzy Membership Functions, Operations on Fuzzy Sets, Fuzzy Relations, Operations on Fuzzy Relations, Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems

Unit II

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Unit III

GENETIC ALGORITHMS 6 Chromosome Encoding Schemes -Population initialization and selection methods – Evaluation function – Genetic operators- Cross over – Mutation – Fitness Function – Maximizing function

Unit IV

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Unit V

APPLICATIONS 6 Modeling a two input sine function – Printed Character Recognition – Fuzzy filtered neural networks – Plasma Spectrum Analysis – Hand written neural recognition – Soft Computing for Color Recipe Prediction.

Course Outcomes:

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Practical Exercises

1. Implementation of fuzzy control/ inference system
2. Programming exercise on classification with a discrete perceptron
3. Implementation of XOR with backpropagation algorithm
4. Implementation of self organizing maps for a specific application
5. Programming exercises on maximizing a function using Genetic algorithm
6. Implementation of two input sine function
7. Implementation of three input non linear function

Text Books:

1. SaJANG, J.-S. R., SUN, C.-T., & MIZUTANI, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ, Prentice Hall,1997
2. Himanshu Singh, Yunis Ahmad Lone, Deep Neuro-Fuzzy Systems with Python
3. With Case Studies and Applications from the Industry, Apress, 2020

Reference Books:

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of IT 6th Sem, 2021 curriculum do visit IT 6th Sem subject syllabuses for 2021 regulation. For Information Technology 6th Sem semesters scheme and subjects, refer to IT 6th Sem 2021 regulation scheme. The scheme details of Professional Elective-VI for IT 6th Sem is as follows.

Don’t forget to download iStudy App for the latest syllabus, results, class timetable and many more features. In case of questions, don’t feel shy to leave a comment or leave feedback in the iStudy app for faster response.

For the results of Information Technology 6th Sem, kindly visit IT 6th Sem direct results link.

For exam time table of Information Technology (IT), kindly visit Anna University exam timetables.

For Information Technology (IT) notices, kindly visit Anna University notices.

Download the iStudy App for all syllabus and other updates.

For updated syllabus of Information Technology (IT) 2021, kindly visit IT updated syllabus.

Wishing you great luck ahead.

For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of knowledge engineering is as follows.

Course Objectives:

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Unit I

REASONING UNDER UNCERTAINTY 6 Introduction – Abductive reasoning – Probabilistic reasoning: Enumerative Probabilities – Subjective Bayesian view – Belief Functions – Baconian Probability – Fuzzy Probability – Uncertainty methods – Evidence-based reasoning – Intelligent Agent – Mixed-Initiative Reasoning – Knowledge Engineering.

Unit II

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Unit III

ONTOLOGIES – DESIGN AND DEVELOPMENT 6 Concepts and Instances – Generalization Hierarchies – Object Features – Defining Features -Representation – Transitivity – Inheritance – Concepts as Feature Values – Ontology Matching. Design and Development Methodologies – Steps in Ontology Development – Domain Understanding and Concept Elicitation – Modelling-based Ontology Specification.

Unit IV

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Unit V

LEARNING AND RULE LEARNING 6 Machine Learning – Concepts – Generalization and Specialization Rules – Types – Formal definition of Generalization. Modelling, Learning and Problem Solving – Rule learning and Refinement -Overview – Rule Generation and Analysis – Hypothesis Learning.

Practical Exercises:

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Course Outcomes:

At the end of this course, the students will be able to:

1. Understand the basics of Knowledge Engineering.
2. Apply methodologies and modelling for Agent Design and Development.
3. Design and develop ontologies.
4. Apply reasoning with ontologies and rules.
5. Understand learning and rule learning.

Text Books:

1. Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, David A. Schum, Knowledge Engineering Building Cognitive Assistants for Evidence-based Reasoning, Cambridge University Press, First Edition, 2016. (Unit 1 – Chapter 1 / Unit 2 – Chapter 3,4 / Unit 3 – Chapter 5, 6 / Unit 4 – 7 , Unit 5 – Chapter 8, 9 )

Reference Books:

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.

For all Information Technology results, visit Anna University IT all semester results direct link.