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Home / Syllabus / B.Sc. (Data Science) / Fourth Year BSc Data Science Honours / Semester 8 / Soft Computing

B.Sc. (Data Science) Soft Computing Syllabus - Mumbai University

This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Soft Computing Syllabus.pdf
Major · Semester 8 · Fourth Year BSc Data Science Honours · 4 credits · 100 marks

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Syllabus for Soft Computing

Major · Semester 8 · Fourth Year BSc Data Science Honours · 4 credits · 100 marks

Module I: Artificial Neural Network

  • Fundamental concepts, Evolution of neural network, basic model of Artificial Neural Network, Important terminologies, McCulloch Pits neuron, linear separability, Hebb network Supervised Learning Network: Perceptron networks, Adaline, MAdaline, Backpropogation network, Radial Basis Function, Time Delay Network, Functional Link Networks, Tree Neural Network. UnSupervised Learning Networks: Fixed weight competitive nets, Kohonen self- organizing feature maps, learning vectors quantization, counter propogation networks, adaptive resonance theory networks. Associative Memory Networks: Training algorithm for pattern Association, Autoassociative memory network, hetroassociative memory network, bi-directional associative memory, Hopfield networks, iterative autoassociative memory networks, temporal associative memory networks

Module II: Special Networks

  • Simulated annealing, Boltzman machine, Gaussian Machine, Cauchy Machine, Probabilistic neural net, cascade correlation network, cognition network, neo- cognition network, cellular neural network, optical neural network Third Generation Neural Networks: Spiking Neural networks, convolutional neural networks, deep learning neural networks, extreme learning machine model.

Module III: Fuzzy Logic

  • Introduction to Fuzzy Logic, Classical sets, Fuzzy sets, Classical Relations and Fuzzy Relations: Cartesian Product of relation, classical relation, fuzzy relations, tolerance and equivalence relations, non-iterative fuzzy sets. Membership Function: features of the membership functions, fuzzification and methods of membership value assignments. Defuzzification: Lambda-cuts for fuzzy sets, Lambda-cuts for fuzzy relations, Defuzzification methods. Fuzzy Arithmetic and Fuzzy measures: fuzzy arithmetic, fuzzy measures, measures of fuzziness, fuzzy integrals

Module IV: Genetic Algorithm

  • Biological Background, Traditional optimization and search techniques, genetic algorithm and search space, genetic algorithm vs. traditional algorithms, basic terminologies, simple genetic algorithm, general genetic algorithm, operators in genetic algorithm, stopping condition for genetic algorithm flow, constraints in genetic algorithm, problem solving using genetic algorithm, the schema theorem, classification of genetic algorithm, Holland classifier systems, genetic programming, advantages and limitations and applications of genetic algorithm 10 Text Books 1. Artificial Intelligence and Soft Computing by Anandita Das Bhattacharya, SPD, 3rd edition 2018 2. Principles of Soft computing by S.N.Sivanandam S.N.Deepa , Wiley, 3rd edition, 2019 3. Neuro-Fuzzy and Soft Computing by J.S.R.Jang, C.T.Sun and E.Mizutani, Prentice Hall of India, 1st edition, 2004 11 Reference Books 1. Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications by S.Rajasekaran, G. A. Vijayalakshami , Prentice Hall of India, 1st edition, 2004 2. Fuzzy Logic with Engineering Applications, Timothy J.Ross, McGrawHill 1st edition, 1997 3. Genetic Algorithms: Search, Optimization and Machine Learning by Davis E.Goldberg, Addison Wesley, 1st edition, 1989 4. Introduction to AI and Expert System by Dan W. Patterson, Prentice Hall of India, 2nd edition, 2009 12 Internal Continuous Assessment: 50% Semester End Examination: 50%

Text Books

  • 1 Artificial Intelligence and Soft Computing by Anandita Das Bhattacharya, SPD, 3rd edition 2018
  • 2 Principles of Soft computing by S.N.Sivanandam S.N.Deepa , Wiley, 3rd edition, 2019
  • 3 Neuro-Fuzzy and Soft Computing by J.S.R.Jang, C.T.Sun and E.Mizutani, Prentice Hall of India, 1st edition, 2004
  • 1 Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications by S.Rajasekaran, G. A. Vijayalakshami , Prentice Hall of India, 1st edition, 2004
  • 2 Fuzzy Logic with Engineering Applications, Timothy J.Ross, McGrawHill 1st edition, 1997
  • 3 Genetic Algorithms: Search, Optimization and Machine Learning by Davis E.Goldberg, Addison Wesley, 1st edition, 1989
  • 4 Introduction to AI and Expert System by Dan W. Patterson, Prentice Hall of India, 2nd edition, 2009

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read
PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
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