Learn · Introduction to Autonomous Systems
Lecture 9 of 82Week 2
Lecture 9: Definition of Intelligence
Prof. Ashok Joshi · IIT Bombay
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- Introduction to Autonomous Systems, IIT Bombay (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures
- Lecture 1: Introduction to the Subject
- Lecture 2: Course Plan
- Lecture 3: Concept of Systems
- Lecture 4: Science of Systems
- Lecture 5: Autonomy Concept
- Lecture 6: Implication of Autonomy
- Lecture 7: Autonomy and Automation
- Lecture 8: Concept of Intelligence
- Lecture 9: Definition of Intelligence
- Lecture 10: Concept of Artificial Intelligence
- Lecture 11: Rational and Human-like Approaches
- Lecture 12: Evolution of Artificial Intelligence
- Lecture 12A: Elements of Artificial Intelligence - 1
- Lecture 12B: Elements of Artificial Intelligence - 2
- Lecture 13: Current Status of Artificial Intelligence
- Lecture 14: Concept of Agent
- Lecture 15: Agent Environment
- Lecture 15A: Complexity Implementation
- Lecture 16: Agent Architecture and Agent Program
- Lecture 17: Simple Reflex Agent Program
- Lecture 18: Model and Goal-based Agent Programs
- Lecture 19: Utility-based and Learning Agent Programs
- Lecture 20: Knowledge-based Agent Concept
- Lecture 21: Concept of Logic and Logical Reasoning
- Lecture 22: Propositional Logic Principles
- Lecture 23: Concept of Uncertain Reasoning
- Lecture 24: Basics of Probability Calculus
- Lecture 25: Concept of Conditional Probability
- Lecture 26: Interpretation of Bayesian Technique
- Lecture 27: Concepts Odds, Likelihood and Utility
- Lecture 28: Concept of Search
- Lecture 29: Blind Search Techniques
- Lecture 30: Informed (Heuristic) Search Techniques
- Lecture 31: Concept of A-star Search
- Lecture 32: Concept of Learning
- Lecture 33: Concept of Supervised Learning
- Lecture 34: Function Fits and Concept of Regression
- Lecture 35: Reinforcement Learning Concept
- Lecture 36: Concept of Unsupervised Clustering
- Lecture 37: Uncertain and Bayesian Learning
- Lecture 38: Artificial Neural Network Concept
- Lecture 39: Multi-layer ANN and Concept of Deep Learning
- Lecture 40: Classification of Numerical Data
- Lecture 41: Non-parametric Models
- Lecture 42: Support Vector Machine and Ensemble Learning
- Lecture 43: Concept of Multi-agent Systems
- Lecture 44: Description of Multi-agent Systems
- Lecture 45: Interaction Modes of Agents
- Lecture 46: Concept of Games
- Lecture 47: Introduction to Autnomous Vehicles
- Lecture 48: Unmanned Vehicle Concept
- Lecture 49: Wheeled Vehicle Motion Model
- Lecture 50: Sensors and Their Characteristics
- Lecture 51: Typical Sensors for Autonomous Vehicles
- Lecture 52: Triangulation and Visual Sensing
- Lecture 53: Feature Extraction and Motion Model
- Lecture 54: Concept of Localization
- Lecture 55: Behaviourism and Concept of Map
- Lecture 56: Belief Model and Map Features
- Lecture 57: Map Building Process
- Lecture 58: Concept of Path Planning
- Lecture 59: Visibility Graph and Voronoi Diagrams
- Lecture 60: Cell and Grid-based Path Planning Techniques
- Lecture 61: Potential Field-based Path Planning
- Lecture 62: Collision Avoidance Strategies
- Lecture 63: Concept of Real-time
- Lecture 64: Real-time Punctuality
- Lecture 65: Evolution of Real-time Systems
- Lecture 66: Basics of Computer Hardware Including the Processor
- Lecture 67: Input/output Devices and Interrupts
- Lecture 68: Concept of Memory
- Lecture 69: Advanced Processing and Memory
- Lecture 69A: Microprocessors and Microcontrollers
- Lecture 70: Concept of Real-time Operating Systems
- Lecture 71: Rate Monotonic Theory
- Lecture 72: Benchmarking of Real-time Operating Systems
- Lecture 73: Real-time Operating System Selection Process
- Lecture 74: Field Programmable Gate Arrays and Distributed Systems
- Lecture 75: Concept of Programming Languages
- Lecture 76: Language Constructs and Comparison of Languages
- Lecture 77: Real-time Compatibility of Languages
- Lecture 78: Course Conclusion