Ai and Soft Computing detailed syllabus for Master of Computer Applications (MCA), 2017 regulation has been taken from the University of Mumbai official website and presented for the MCA students. For Scheme related information like 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 Master of Computer Applications (MCA) 4th Sem scheme, 2017 regulation, do visit MCA 4th Sem 2017 Pattern Scheme. For the Elective-2 scheme of 4th Sem 2017 regulation (MCA), refer to MCA 4th Sem 2017 Pattern Elective-2 Scheme. The detailed syllabus for ai and soft computing is as follows.
MCA4054: Ai and Soft Computing Syllabus for MCA 4th Sem 2017 Pattern Mumbai University (Elective-2)
Prerequisites:
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Course Educational Objectives (CEO):
At the end of the course, the students will be able to
- Identify and describe problems that are amenable to solution by AI methods.
- Study appropriate soft computing techniques for problem solving
- Study optimization techniques based on soft computing approach
Course Outcomes:
At the end of the course, the students will be able to
- Understand various AI concepts
- Solve the problems using neural networks techniques.
- Apply fuzzy logic techniques to find solution of uncertain problems.
- Analyze the genetic algorithms and their applications
1. Introduction to AI
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2. Problem Solving
Problems, problem spaces and search: Define the problem as a state space search, Production systems, Problem characteristics, Production system characteristic, Issues in design of search program Search Techniques: DFS, BFS, Hill Climbing 06
3. Knowledge Representation
Knowledge Representation: Need to represent knowledge, Knowledge representation with mapping scheme, Properties of good knowledge-based system, Knowledge representation issues, AND-OR graph, Types of knowledge 09
4. Concepts of Soft Computing
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5. Neural Network
Artificial Neural Network: Introduction,Fundamental Concept, Artificial Neural Network, Brain vs. Computer – Comparison Between Biological Neuron and Artificial Neuron, Basic Models of Artificial Neural Network Supervised Learning Network-Linear Separability, Perceptron Networks, Adaptive Linear Neuron (Adaline), Multiple Adaptive Linear Neurons, Back-Propagation Network. Unsupervised Learning Networks- MaxNet 12
6. Fuzzy Logic
Introduction to Fuzzy Logic, Classical Sets and Fuzzy Sets:Introduction to Fuzzy Logic, Classical Sets (Crisp Sets),Fuzzy Sets Classical Relations and Fuzzy Relations: Introduction, Cartesian Product of Relation, Classical Relation, Fuzzy Relations Membership Functions: Introduction, Features of the Membership Functions, Fuzzification, Methods of Membership Value Assignments Defuzzification: Introduction, Lambda-Cuts for Fuzzy Sets (Alpha-Cuts), Lambda-Cuts for Fuzzy Relations, Defuzzification Methods 10
7. Fuzzy Inference System
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8. Genetic Algorithm
Genetic Algorithm: Basic concepts, Difference between genetic algorithm and traditional methods, Simple genetic algorithm, Working principle, Procedures of GA, Genetic operators-reproduction, Mutation, crossover. 04
Reference Books:
- Artificial Intelligence, 3rd Edition, Elaine Rich, Kevin Knight, S.B. Nair, Tata McGraw Hill.
- Artificial Intelligence and Soft Computing for Beginners- Anandita Das, ShroffPublication.
- Dr. S. N. Sivanandam and Dr. S. N. Deepa,Principles of Soft Computing John Wiley
- S. Rajsekaran& G.A. VijayalakshmiPai, Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis and ApplicationsPrentice Hall of India.
- Kumar Satish, Neural NetworksTata McGraw Hill
- Timothy J. Ross, Fuzzy Logic with Engineering ApplicationsWiley India.
- Search, Optimization & Machine Learning by David E. Goldberg.
Assessment:
Internal: Assessment consists of two tests (T1 and T2) .The final marks should be the average of the two tests. End Semester Theory Examination: Guidelines for setting up the question paper.
- Question paper will comprise of total six questions.
- Question Number One should be compulsory.
- All question carry equal marks.
- Students can attempt any three from the remaining.
- Questions will be mixed in nature (for example supposed Q.2 has part a from module 3 then part b will be from any module other than module 3).
In question paper weightage of each module will be proportional to number of respective lecture hours as mention in the syllabus.
For detail Syllabus of all subjects of MCA 4th Sem, 2017 regulation, visit MCA 4th Sem Subjects of 2017 Pattern.