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: Problem Solving by SearchUnit 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: Uncertainty and PlanningUnit 5: Learning and Applications
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 3 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.
सिलेबस · पाठ्यक्रम
1 subject · 5 chapters · 26 topics
01Unit 1: Introduction and Intelligent Agents
5 टॉपिक+
- Definition, history and applications of AI
- Turing test
- Agents and environments; PEAS description
- Types of environments
- Agent types: simple reflex, model-based, goal-based, utility-based, learning
02Unit 2: Problem Solving by Search
6 टॉपिक+
- Problem formulation and state space
- Uninformed search: BFS, DFS, uniform cost, iterative deepening
- Informed search: greedy best-first, A* and heuristics
- Local search: hill climbing, simulated annealing, genetic algorithms
- Adversarial search: minimax and alpha-beta pruning
- Constraint satisfaction problems
03Unit 3: Knowledge Representation and Reasoning
5 टॉपिक+
- Propositional logic and inference
- First-order predicate logic
- Unification and resolution
- Forward and backward chaining
- Semantic networks, frames and ontologies
04Unit 4: Uncertainty and Planning
5 टॉपिक+
- Probability basics and Bayes' rule
- Bayesian belief networks
- Fuzzy sets and fuzzy logic
- Classical planning and STRIPS
- Expert systems: architecture and examples
05Unit 5: Learning and Applications
5 टॉपिक+
- Forms of learning: supervised, unsupervised, reinforcement
- Decision tree learning
- Artificial neural networks basics
- Reinforcement learning basics
- Natural language processing and robotics overview
About this course
आप यह कर पाएँगे
- Define AI and describe intelligent agents and their environments using PEAS
- Solve problems with uninformed, informed, local and adversarial search
- Represent knowledge in propositional and first-order logic and apply resolution
- Reason under uncertainty using Bayes' rule, Bayesian networks and fuzzy logic
- Explain planning, expert systems and basic natural language processing
- Describe supervised, unsupervised and reinforcement learning
यह किसके लिए है
- MCA students of Semester 3 (AKTU, Lucknow)
- Students preparing for UGC-NET / GATE and AI-related job interviews
आपको क्या चाहिए
- Data structures (graphs, trees) and basic probability
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
सवाल
Which syllabus does this Artificial Intelligence course follow?
It follows the AKTU (Lucknow) MCA syllabus for Semester 3. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.
Is programming needed for this paper?
The theory paper has no coding, but the Artificial Intelligence Lab uses Python and the centre teaches both together.
How are students tested at the centre?
Unit-wise MCQ tests, numerical / problem practice, previous AKTU question papers and a final mock paper before the semester exam.
