Artificial Intelligence (B.Tech)

Search techniques, knowledge representation, reasoning, machine learning basics and expert systems.

6 MonthsDuration 5Chapters28Topics

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Syllabus · पाठ्यक्रम

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01Unit 1: Introduction and Intelligent Agents 5 topics

Definition and history of AI, future of AI
Intelligent agents and PEAS description
Types of agents: reflex, model-based, goal-based, utility-based, learning
Environments and their properties
Problem solving approach to typical AI problems

02Unit 2: Search Methods and Game Playing 6 topics

Problem-solving agents and state space
Uninformed search: BFS, DFS, uniform cost, iterative deepening
Heuristics and informed search: greedy best-first, A*
Local search: hill climbing, simulated annealing
Constraint satisfaction problems and backtracking
Adversarial search: minimax, alpha-beta pruning, stochastic games

03Unit 3: Knowledge Representation and Reasoning 6 topics

First-order predicate logic
Prolog programming
Unification, forward and backward chaining, resolution
Ontological engineering: categories, objects and events
Reasoning with default information
Bayes' rule and Bayesian networks

04Unit 4: Machine Learning 5 topics

Supervised and unsupervised learning
Decision trees and information gain
Statistical learning: Naive Bayes
Learning with hidden data: EM algorithm
Reinforcement learning

05Unit 5: Pattern Recognition and Expert Systems 6 topics

Design of a pattern recognition system
Statistical pattern recognition and parameter estimation
Dimension reduction: PCA and LDA
Classification: nearest neighbour, Bayes classifier, SVM
Clustering: K-means
Expert systems: architecture and applications

About

You will be able to

Explain the foundations of AI and the types of intelligent agents (PEAS)
Apply uninformed and informed search (BFS, DFS, A*, hill climbing) and constraint satisfaction
Use minimax with alpha-beta pruning for game playing
Represent knowledge in first-order logic and reason with unification, resolution and chaining
Apply Bayes' rule and Bayesian networks to uncertain problems
Explain supervised, unsupervised and reinforcement learning, pattern recognition and expert systems

Who it is for

B.Tech Computer Science & Engineering, Information Technology students of Semester 7 (AKTU)
Students of other universities studying the same subject who want unit-wise coaching

What you need

Data Structure and Discrete Mathematics
Basic probability and Python programming

Questions

Which syllabus does the Artificial Intelligence course follow?

It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Artificial Intelligence. If your own university's syllabus differs, the centre follows your university syllabus.

Will this help in the university examination?

Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.

Is programming needed for this theory subject?

Not for the theory exam, but small Python demonstrations of search, minimax and learning are shown to make the concepts clear.