Artificial Intelligence
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Artificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence (subject code 4341707) as prescribed for PE · Semester 4 divides the course into 6 units, covering 37 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (4341707)Unit 1 Introduction of Artificial Intelligence 1a.Explain concept and importance of Artificial Intelligence
- 1.1. Introduction to AI
- 1.2. The concept and importance of AI
- 1.3. Human intelligence vs. Machine intelligence
- 1.4. Agents and Environments, the concept of rationality, the nature of environments, and structure of agents. Artificial Intelligence Course Code: 4341707
Unit 2 Expert Systems 2a.Explain advantages and disadvantages of expert system
- 2.1. Expert systems: advantages, disadvantages
- 2.2. Expert system architecture
- 2.3. Functions of various parts
- 2.4. Mechanism and role of inference engine
- 2.5. Types of Expert system
- 2.6. Tuning of expert systems
- 2.7. Role of Expert systems in instrumentation and process control
Unit 3 Artificial Neural Networks 3a.Explain function of single neuron
- 3.1. Structure and function of a single neuron
- 3.2. Artificial neuron models
- 3.3. Types of activation functions
- 3.4. Neural network architectures
- 3.5. Neural learning, Evaluation of networks
- 3.6. Supervised learning, Back propagation take all networks,
- 3.7. Application of neural networks for Classification−algorithm
- 3.8. Unsupervised learning, winner Clustering, Pattern associations, Function approximation, Forecasting etc
Unit 4 Fuzzy Logic 4a.Introduce fuzzy logic
- 4.1. Introduction to Fuzzy Logic
- 4.2. Conventional set vs. Fuzzy set
- 4.3. Operations of fuzzy set
- 4.4. Membership function
- 4.5. Fuzzy rules, Fuzzy inference
- 4.7. Application for control Artificial Intelligence Course Code: 4341707
Unit 5 Basics of Machine Learning 5a.Ellaborate basics of Machine Learning
- 5.1. Introduction to Machine Learning
- 5.5. Supervised learning (Linear Regression, Decision Trees, Learning Decision Trees, K- nearest Neighbor)
- 5.6. Unsupervised learning (K-means, Dimensionality reduction, Linear Discriminate Analysis(LDA)
- 5.7. Reinforcement learning
Unit 6 Natural Language Processing 6a.Explain concept of natural language processing
- 6.1. Introduction to NLP
- 6.2. Syntactic Processing
- 6.3. Semantic Analysis
- 6.4. Discourse and Pragmatic Processing, Text Analytics, Text pre-processing
- 6.5. Bag of Words, Word Cloud
- 6.6. Machine Translation
- 6.7. Sentiment analysis
- 6.8. AI Chatbots Note: The UOs need to be formulated at the ‘Application Level’ and above of Revised Bloom’s Taxonomy’ to accelerate the attainment of the COs and the competency.
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