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Course10910100582 lectures

Introduction to Autonomous Systems

Prof. Ashok Joshi · IIT Bombay

Start with lecture 1 At NPTEL

About

In recent times, technological advances have been focusing on systems that are capable of functioning with no or minimal human intervention and are generically grouped together under overall identifier of ‘Autonomous Systems’. Present course deals with basic aspects and behavioural patterns of autonomous intelligent systems in order to understand issues and processes for their synthesis and in this regard presents concepts of artificial intelligence, real-time systems, logical and uncertain reasoning methods, search and learning techniques and basic concepts of game theory.

Intended audience: Students and Teachers

Prerequisites: Basics of Linear Algebra, Probability and Statistics, Mathematical/Numerical Analysis

Lectures

Week 1

  1. Lecture 1: Introduction to the Subject YouTube
  2. Lecture 2: Course Plan YouTube
  3. Lecture 3: Concept of Systems YouTube
  4. Lecture 4: Science of Systems YouTube
  5. Lecture 5: Autonomy Concept YouTube
  6. Lecture 6: Implication of Autonomy YouTube
  7. Lecture 7: Autonomy and Automation YouTube

Week 2

  1. Lecture 8: Concept of Intelligence YouTube
  2. Lecture 9: Definition of Intelligence YouTube
  3. Lecture 10: Concept of Artificial Intelligence YouTube
  4. Lecture 11: Rational and Human-like Approaches YouTube
  5. Lecture 12: Evolution of Artificial Intelligence YouTube
  6. Lecture 12A: Elements of Artificial Intelligence - 1 YouTube
  7. Lecture 12B: Elements of Artificial Intelligence - 2 YouTube

Week 3

  1. Lecture 13: Current Status of Artificial Intelligence YouTube
  2. Lecture 14: Concept of Agent YouTube
  3. Lecture 15: Agent Environment YouTube
  4. Lecture 15A: Complexity Implementation YouTube
  5. Lecture 16: Agent Architecture and Agent Program YouTube
  6. Lecture 17: Simple Reflex Agent Program YouTube
  7. Lecture 18: Model and Goal-based Agent Programs YouTube
  8. Lecture 19: Utility-based and Learning Agent Programs YouTube

Week 4

  1. Lecture 20: Knowledge-based Agent Concept YouTube
  2. Lecture 21: Concept of Logic and Logical Reasoning YouTube
  3. Lecture 22: Propositional Logic Principles YouTube
  4. Lecture 23: Concept of Uncertain Reasoning YouTube
  5. Lecture 24: Basics of Probability Calculus YouTube

Week 5

  1. Lecture 25: Concept of Conditional Probability YouTube
  2. Lecture 26: Interpretation of Bayesian Technique YouTube
  3. Lecture 27: Concepts Odds, Likelihood and Utility YouTube
  4. Lecture 28: Concept of Search YouTube
  5. Lecture 29: Blind Search Techniques YouTube
  6. Lecture 30: Informed (Heuristic) Search Techniques YouTube
  7. Lecture 31: Concept of A-star Search YouTube

Week 6

  1. Lecture 32: Concept of Learning YouTube
  2. Lecture 33: Concept of Supervised Learning YouTube
  3. Lecture 34: Function Fits and Concept of Regression YouTube
  4. Lecture 35: Reinforcement Learning Concept YouTube
  5. Lecture 36: Concept of Unsupervised Clustering YouTube
  6. Lecture 37: Uncertain and Bayesian Learning YouTube
  7. Lecture 38: Artificial Neural Network Concept YouTube

Week 7

  1. Lecture 39: Multi-layer ANN and Concept of Deep Learning YouTube
  2. Lecture 40: Classification of Numerical Data YouTube
  3. Lecture 41: Non-parametric Models YouTube
  4. Lecture 42: Support Vector Machine and Ensemble Learning YouTube
  5. Lecture 43: Concept of Multi-agent Systems YouTube
  6. Lecture 44: Description of Multi-agent Systems YouTube
  7. Lecture 45: Interaction Modes of Agents YouTube
  8. Lecture 46: Concept of Games YouTube

Week 8

  1. Lecture 47: Introduction to Autnomous Vehicles YouTube
  2. Lecture 48: Unmanned Vehicle Concept YouTube
  3. Lecture 49: Wheeled Vehicle Motion Model YouTube
  4. Lecture 50: Sensors and Their Characteristics YouTube
  5. Lecture 51: Typical Sensors for Autonomous Vehicles YouTube
  6. Lecture 52: Triangulation and Visual Sensing YouTube
  7. Lecture 53: Feature Extraction and Motion Model YouTube
  8. Lecture 54: Concept of Localization YouTube

Week 9

  1. Lecture 55: Behaviourism and Concept of Map YouTube
  2. Lecture 56: Belief Model and Map Features YouTube
  3. Lecture 57: Map Building Process YouTube
  4. Lecture 58: Concept of Path Planning YouTube
  5. Lecture 59: Visibility Graph and Voronoi Diagrams YouTube
  6. Lecture 60: Cell and Grid-based Path Planning Techniques YouTube
  7. Lecture 61: Potential Field-based Path Planning YouTube
  8. Lecture 62: Collision Avoidance Strategies YouTube

Week 10

  1. Lecture 63: Concept of Real-time YouTube
  2. Lecture 64: Real-time Punctuality YouTube
  3. Lecture 65: Evolution of Real-time Systems YouTube
  4. Lecture 66: Basics of Computer Hardware Including the Processor YouTube
  5. Lecture 67: Input/output Devices and Interrupts YouTube

Week 11

  1. Lecture 68: Concept of Memory YouTube
  2. Lecture 69: Advanced Processing and Memory YouTube
  3. Lecture 69A: Microprocessors and Microcontrollers YouTube
  4. Lecture 70: Concept of Real-time Operating Systems YouTube
  5. Lecture 71: Rate Monotonic Theory YouTube
  6. Lecture 72: Benchmarking of Real-time Operating Systems YouTube

Week 12

  1. Lecture 73: Real-time Operating System Selection Process YouTube
  2. Lecture 74: Field Programmable Gate Arrays and Distributed Systems YouTube
  3. Lecture 75: Concept of Programming Languages YouTube
  4. Lecture 76: Language Constructs and Comparison of Languages YouTube
  5. Lecture 77: Real-time Compatibility of Languages YouTube
  6. Lecture 78: Course Conclusion YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Bombay
Department
Humanities and Social Sciences
Teacher
Prof. Ashok Joshi
Level
Undergraduate
Language
English
Subjects
Society and politics
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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