Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Introduction and Intelligent AgentsUnit 2: Search Methods and Game PlayingUnit 3: Knowledge Representation and Reasoning
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Machine LearningUnit 5: Pattern Recognition and Expert Systems
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 7 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 28 topics
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 this course
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
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
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.
