* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download lesson plan
Survey
Document related concepts
Affective computing wikipedia , lookup
Knowledge representation and reasoning wikipedia , lookup
Neural modeling fields wikipedia , lookup
Catastrophic interference wikipedia , lookup
Hierarchical temporal memory wikipedia , lookup
Genetic algorithm wikipedia , lookup
Convolutional neural network wikipedia , lookup
Concept learning wikipedia , lookup
Pattern recognition wikipedia , lookup
History of artificial intelligence wikipedia , lookup
Type-2 fuzzy sets and systems wikipedia , lookup
Machine learning wikipedia , lookup
Transcript
DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Unit: I Branch: M.E.(C.S.) Semester: II Page 01 of 06 Unit – I syllabus: INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS 9 Evolution of Computing - Soft Computing Constituents – From Conventional AI to Computational Intelligence - Machine Learning Basics Objective: To impart knowledge on origin and basics of soft computing and Neural Networks. Session No Topics to be covered Time Allocation (min) Books Referred Teaching Method 1 Overview on the course and Introduction to Soft Computing(SC) 50 1,2,3,5,8 BB 2 Evolution of Computing 50 1,2,3,5,8 BB 3 Soft Computing Constituents – Historical Sketch and traditional AI 50 1 BB 4 Soft Computing techniques and structure 50 1,2,3,5,8 BB 5 Comparative characteristics of Constituents of SC 50 1 BB 6 Neuro – Fuzzy and SC characteristics 50 1 BB 7 From Conventional AI to Computational Intelligence 50 1 BB Machine Learning Basics 50 1,2,3,5,8 BB CAT-I 40 8,9 DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Unit: II Branch: M.E.(C.S.) Semester: II Page 02 of 06 Unit – II syllabus: GENETIC ALGORITHMS 9 Introduction to Genetic Algorithms (GA) – Applications of GA in Machine Learning – Machine Learning Approach to Knowledge Acquisition. Objective : To impart knowledge on genetic algorithms and their applications. Session No Topics to be covered Time Allocation (min) Books Referred Teaching Method 10 Introduction to GA 50 5 BB 11 Mathematical foundation 50 5 BB 12,13 Implementation of GA 50 5 BB 14,15 Application of GA 50 5 BB 15 Introduction to genetic based machine learning 50 5 BB 16 Application of GA in machine learning 50 5 BB Machine Learning Approach to Knowledge Acquisition. 50 5 BB CAT-II 40 17,18 DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Unit: III Branch: M.E.(C.S.) Semester: II Page 03 of 06 Unit – III syllabus: NEURAL NETWORKS 9 Machine Learning Using Neural Network, Adaptive Networks – Feed forward Networks – Supervised Learning Neural Networks – Radial Basis Function Networks - Reinforcement Learning – Unsupervised Learning Neural Networks – Adaptive Resonance architectures – Advances in Neural networks. Objective: To impart knowledge on various types of neural networks, learning methods and their applications Session No Topics to be covered Time Allocation (min) Books Referred Teaching Method 19 Machine Learning Using Neural Network 50 1,3,8 BB 20 Adaptive Networks 50 1,3,8 BB 21 Feed forward Networks 50 1,3,8 BB Supervised Learning Neural Networks 50 1,3,8 BB 24 Radial Basis Function Networks 50 1,3,8 BB 25 Reinforcement Learning 50 1,3,8 BB 26,27 Unsupervised Learning Neural Networks 50 1,3,8 BB 28,29 Adaptive Resonance architectures 50 1,3,8 BB Advances in Neural networks 50 1,3,8 BB CAT-III 40 22,23 30 DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Unit: IV Branch: M.E.(C.S.) Semester: II Page 04 of 06 Unit – IV syllabus: FUZZY LOGIC 9 Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy Relations – Membership Functions- Fuzzy Rules and Fuzzy Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems – Fuzzy Decision Making Objective: To impart knowledge on fuzzy logic and different stages in fuzzy systems Session No Topics to be covered Time Allocation (min) Books Referred Teaching Method 31 Fuzzy Sets 50 2 BB 32 Operations on Fuzzy Sets 50 2 BB 33 Fuzzy Relations 50 2 BB 34 Membership Functions 50 2 BB 35 Fuzzy Rules 50 2 BB 36 Fuzzy Reasoning 50 2 BB 37 Fuzzy Inference Systems 50 2 BB 38 Fuzzy Expert Systems 50 2 BB 39 Fuzzy Decision Making 50 2 BB CAT-IV 40 DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Unit: V Branch: M.E.(C.S.) Semester: II Page 05 of 06 Unit – V syllabus: NEURO-FUZZY MODELING 9 Adaptive Neuro-Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modeling – Classification and Regression Trees – Data Clustering Algorithms – Rulebase Structure Identification – Neuro-Fuzzy Control – Case studies Objective : To impart knowledge on various stages in Neuro-Fuzzy Modeling Time Allocation (min) Books Referred Teaching Method 50 1 BB 50 1 BB 50 1 BB Analysis of adaptive learning capability 50 1 BB Classification and Regression Trees 50 1 BB 50 1 BB 50 1 BB Neuro-Fuzzy Control – I & II and case studies 50 1 BB Review 50 1-8 BB CAT-V 40 Session No 40 41 42 43 44,45 46,47 48 49.50 51 Topics to be covered Adaptive Neuro-Fuzzy Inference Systems – ANFIS architecture Hybrid learning algorithm Coactive Neuro-Fuzzy Modeling – Frame work and neuron functions Data Clustering Algorithms: K- means, Fuzzy C-means, Mountain and subtractive clustering Rulebase Structure Identification organization DOC/LP/01/28.02.02 LESSON PLAN LP- CP 9254 LP Rev. No: 00 Sub Code & Name: CP 9254 - SOFT COMPUTING Date: 12/02/2012 Branch: M.E.(C.S.) Semester: II Page 06 of 06 Course Delivery Plan: Week UNIT 1 2 3 4 5 6 7 I II I II I II I II I II I II I II C C A A 1 1 1 1 1 2 2 2 2 2 3 3 T T 1 2 8 9 10 11 12 13 14 15 I II I II I II I II I II I II I II I II C C C A A A 3 3 3 4 4 4 4 4 5 5 5 5 5 T T T 3 4 5 TEXT BOOKS: 1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”, PrenticeHall of India, 2003 2. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”,Prentice Hall, 1995 3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques”, Pearson Edn., 2003 REFERENCES: 4. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998 5. David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison Wesley, 1997 6. S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Fuzzy Logic using MATLAB”, Springer, 2007. 7. S.N.Sivanandam · S.N.Deepa, “ Introduction to Genetic Algorithms”, Springer, 2007. 8. Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS Publishers, 1992. Prepared by Approved by S.P.Sivagnana Subramanian Prof. E.G.Govindan Assistant Professor - EC Vice Principal & HOD-EC Signature Name Designation Date