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ME-703 (B) RGPV B.Tech

Artificial Intelligence Techniques (AIT)

The official syllabus is free to read, no account needed.

Syllabus, notes and important questions for ME-703 (B)

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Introduction to Artificial Intelligence 10 topics
  • Main components and characteristics of AI (Feature Engineering, $ANN$, Deep Learning)
  • Applications of AI
  • Advantages and disadvantages of AI
  • Goals of AI
  • Comparison of programming a system with AI versus without AI
  • Challenges in AI
  • Programming languages commonly used in AI
  • Techniques/Algorithms used in AI
  • AI software platforms
  • Future trends in AI
Unit 1 notes
Unit 2 Various Types of Production Systems and Search Techniques 8 topics
  • Types of production systems
  • Characteristics of production systems
  • Breadth‑first search vs. depth‑first search
  • Hill‑climbing search technique
  • Best‑first search
  • $A^{*}$ algorithm
  • $AO^{*}$ algorithm
  • Various control strategies for production systems
Unit 2 notes
Unit 3 Knowledge Representation and Probabilistic Reasoning 11 topics
  • Problems in knowledge representation
  • Knowledge representation using propositional logic
  • Knowledge representation using predicate logic
  • Comparison of propositional and predicate logic
  • Resolution, refutation, deduction, theorem proving, inferencing
  • Monotonic and non‑monotonic reasoning
  • Probabilistic reasoning
  • Bayes' theorem
  • Semantic networks, scripts, schemas, frames, conceptual dependency
  • Fuzzy logic
  • Forward and backward reasoning
Unit 3 notes
Unit 4 Game Playing Techniques 5 topics
  • Minimax procedure
  • $\alpha$‑$\beta$ pruning
  • Planning techniques
  • Block‑world problem in robotics
  • Introduction to natural language processing
Unit 4 notes
Unit 5 Introduction to Learning, ANN 11 topics
  • Learning techniques in AI
  • Introduction to artificial neural networks (ANN)
  • Common‑sense reasoning
  • Convolutional Neural Network ($CNN$)
  • Feedforward Neural Network ($FNN$)
  • Recurrent Neural Network ($RNN$)
  • Multilayer Perceptron
  • Three‑layer architecture of ANN
  • Implementation of ANN
  • Applications of ANN in images, signals and language
  • Examples of expert systems
Unit 5 notes
Read the full syllabus

Exam insights

Counted from 4 past ME-703 (B) papers.

Marks asked per paper, by unit

  • Unit 1 Introduction to Artificial Intelligence 12 marks
  • Unit 2 Various Types of Production Systems and Search Techniques 14 marks
  • Unit 3 Knowledge Representation and Probabilistic Reasoning 13 marks
  • Unit 4 Game Playing Techniques 10 marks
  • Unit 5 Introduction to Learning, ANN 15 marks

Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.

Asked again and again

  1. Explain the working of the A* search algorithm and its admissible heuristic.

    Asked 4 times: 2022, 2023, 2024, 2025

  2. Discuss the role of knowledge representation techniques such as semantic networks and frames in AI.

    Asked 3 times: 2022, 2023, 2024

  3. Derive the back‑propagation weight update rule for a single neuron with sigmoid activation.

    Asked 4 times: 2022, 2023, 2024, 2025

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