CSE 8th SEM Notes CSVTU

On which topics Chhattisgarh Swami Vivekananda Technical University, Bhilai B.Tech Computer Science & Engineering notes based, will you please provide here CSVTU B.Tech Computer Science & Engineering course detailed syllabus, bcoz of some reason I was not attend all classes so now need short notes to prepare for exam???

Can you provide me the syllabus of 8th Sem of B Tech in Computer Science & Engineering Program offered by CSVTU (Chhattisgarh Swami Vivekanad Technical University)?

[pooja]

The syllabus of 8th Sem of B Tech in Computer Science & Engineering Program offered by CSVTU (Chhattisgarh Swami Vivekanad Technical University) is as follows:

Subject: Artificial Intelligence & Expert Systems Code: 322831(22)

UNIT I Overview & Search Techniques:
Introduction to AI, Problem Solving, State space search, Blind search: Depth first search, Breadth first search, Informed search: Heuristic function, Hill climbing search, Best first search, A* & AO* Search, Constraint satisfaction. Game tree, Evaluation function, Mini-Max search, Alpha-beta pruning, Games of chance.

UNIT II Knowledge Representation (KR):
Introduction to KR, Knowledge agent, Predicate logic, WFF, Inference rule & theorem proving forward chaining, backward chaining, resolution; Propositional knowledge, Boolean circuit agents. Rule Based Systems, Forward reasoning: Conflict resolution, backward reasoning: Use of Back tracking, Structured KR: Semantic Net - slots, inheritance, Frames- exceptions and defaults attached predicates, Conceptual Dependency formalism and other knowledge representations.

UNIT III Handling uncertainty & Learning:
Source of uncertainty, Probabilistic inference, Bayes theorem, Limitation of naive Bayesian system, Bayesian Belief Network (BBN), Inference with BBN, Dempster-Shafer Theory, Fuzzy Logic, Fuzzy function, Fuzzy measure, Non monotonic reasoning: Dependency directed backtracking, Truth maintenance systems. Learning: Concept of learning, Learning model, learning decision tree, Paradigms of machine learning, Supervised & Unsupervised learning, Example of learning, Learning by induction, Learning using Neural Networks.

UNIT IV Natural Language Processing (NLP) & Planning:
Overview of NLP tasks, Parsing, Machine translation, Components of Planning System, Planning agent, State-Goal & Action Representation, Forward planning, backward chaining, Planning example: partial-order planner, Block world.

UNIT V Expert System & AI languages:
Need & Justification for expert systems- cognitive problems, Expert System Architectures, Rule based systems, Non production system, knowledge acquisition, Case studies of expert system. Ai language: Prolog syntax, Programming with prolog, backtracking in prolog, Lisp syntax, Lisp programming.

Course outcome: After successful completion of the course, students will be able
Demonstrate fundamental understanding of artificial intelligence (AI) and expert systems. Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.
Demonstrate awareness and a fundamental understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.
Demonstrate proficiency in applying scientific method to models of machine learning

Text Books:-
1. Artificial Intelligence by Elaine Rich and Kevin Knight, Tata MeGraw Hill.
2. Introduction to Artificial Intelligence and Expert Systems by Dan W.Patterson, Prentice Hall of India.
Reference Books :-
1. Principles of Artificial Intelligence by Nils J.Nilsson, Narosa Publishing house.
2. Programming in PROLOG by Clocksin & C.S. Melish, Narosa Publishing house.
3. Rule based Expert Systems-A practical Introduction by M. Sasikumar, S.Ramani, et. al., Narosa Publishing House