CSC703: Artificial Intelligence & Soft Computing Syllabus for CS 7th Sem 2019 Pattern Mumbai University

Artificial Intelligence & Soft Computing detailed syllabus scheme for Computer Engineering (CS), 2019 regulation has been taken from the University of Mumbai official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.

For all other Mumbai University Computer Engineering 7th Sem Syllabus 2019 Pattern, do visit CS 7th Sem 2019 Pattern Scheme. The detailed syllabus scheme for artificial intelligence & soft computing is as follows.

Artificial Intelligence & Soft Computing Syllabus for Computer Engineering BE 7th Sem 2019 Pattern Mumbai University

Course Objectives:

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Course Outcomes:

Students should be able to –

1. Identify the various characteristics of Artificial Intelligence and Soft Computing techniques.
2. Choose an appropriate problem solving method for an agent to find a sequence of actions to reach the goal state.
3. Analyse the strength and weakness of AI approaches to knowledge representation, reasoning and planning.
4. Construct supervised and unsupervised ANN for real world applications.
5. Design fuzzy controller system.
6. Apply Hybrid approach for expert system design.

Prerequisites:

Basic Mathematics, Algorithms

Module 1

Introduction to Artificial Intelligence(AI) and Soft Computing 4

1. Introduction and Definition of Artificial Intelligence.
2. Intelligent Agents : Agents and Environments ,Rationality, Nature of Environment, Structure of Agent, types of Agent
3. Soft Computing: Introduction of soft computing, soft computing vs. hard computing, various types of soft computing techniques.

Module 2

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Module 3

Knowledge, Reasoning and Planning 10

1. Knowledge based agents
2. First order logic: syntax and Semantic, Knowledge Engineering in FOL Inference in FOL : Unification, Forward Chaining, Backward Chaining and Resolution
3. Planning Agent, Types of Planning: Partial Order, Hierarchical Order, Conditional Order

Module 4

Fuzzy Logic 12

1. Introduction to Fuzzy Set: Fuzzy set theory, Fuzzy set versus crisp set, Crisp relation & fuzzy relations, membership functions,
2. Fuzzy Logic: Fuzzy Logic basics, Fuzzy Rules and Fuzzy Reasoning
3. Fuzzy inference systems: Fuzzification of input variables, defuzzification and fuzzy controllers.

Module 5

Artificial Neural Network 12

1. Introduction – Fundamental concept- Basic Models of Artificial Neural Networks – Important Terminologies of ANNs – McCulloch-Pitts Neuron
2. Neural Network Architecture: Perceptron, Single layer Feed Forward ANN, Multilayer Feed Forward ANN, Activation functions, Supervised Learning: Delta learning rule, Back Propagation algorithm.
3. Un-Supervised Learning algorithm: Self Organizing Maps

Module 6

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Text Books:

1. Stuart J. Russell and Peter Norvig, “Artificial Intelligence A Modern Approach Second Edition” Pearson Education.
2. Samir Roy and Chakraborty, Introduction to soft computing, Pearson Edition.
3. S.N.Sivanandam, S.N.Deepa “Principles of Soft Computing” Second Edition, Wiley Publication.
4. S.Rajasekaran and G.A.VijayalakshmiPai “Neural Networks, Fuzzy Logic and Genetic Algorithms” PHI Learning.
5. N.P.Padhy, Artificial Intelligence and Intelligent Systems, Oxford University Press.

Reference Books:

1. Elaine Rich and Kevin Knight Artificial Intelligence Third Edition, Tata McGraw-Hill Education Pvt. Ltd., 2008.
2. Satish Kumar “Neural Networks A Classroom Approach” Tata McGrawHill.
3. Zimmermann H.S “Fuzzy Set Theory and its Applications”Kluwer Academic Publishers.
4. Hagan, Demuth, Beale,”Neural Network Design” CENGAGE Learning, India Edition.
5. J.-S.R.Jang “Neuro-Fuzzy and Soft Computing” PHI 2003.
6. JacekM.Zurada “Introduction to Artificial Neural Sytems” Jaico Publishing House.

Internal Assessment: Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed. Duration of each test shall be one hour. End Semester Theory Examination:

1. Question paper will comprise of 6 questions, each carrying 20 marks.
2. The students need to solve total 4 questions.
3. Question No.1 will be compulsory and based on entire syllabus.
4. Remaining question (Q.2 to Q.6) will be selected from all the modules.

For detail syllabus of all other subjects of Computer Engineering (CS) 7th Sem 2019 regulation, visit CS 7th Sem Subjects syllabus for 2019 regulation.