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Instructor
Dr. Yogesh Kulkarni, Instructor - Introduction to Artificial Intelligence

Dr. Yogesh Kulkarni

16+ years in CAD/Engineering software development, in various capacities, including R & D group and site manager. Recently finished a PhD in Geometric Modeling. Currently working as a Data Analytics consultant, in areas such as Natural Language Processing, Text Mining, Machine Learning and Deep Learning.
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Instructor: Dr. Yogesh Kulkarni

Learn what exactly AI is, what are its different facets.

Basic introduction to, "What is AI?", AI schools of thoughts, Alan Turing's contribution, etc.

Instructor has a Ph.D. in Geometric Modeling along with 16+ years of professional experience in it. He is a consultant and an instructor in AI/ML related subjects for past 3+ years.

Course Description

3+ hours of content geared towards explaining basic ideas behind AI.

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What am I going to get from this course?

  • Show an understanding of basics of Artificial Intelligence and its various facets.
  • Suggest most suitable Intelligent Agent approach in a suitable scenario.
  • Comprehend further topics in the ML/AI track.
     

Prerequisites and Target Audience

What will students need to know or do before starting this course?

• Familiarity with college level mathematics
• Familiarity with Computer Programming, preferably Python and Algorithms.

Who should take this course? Who should not?

Students or professionals with background of college level mathematics and good understanding of algorithms.

Curriculum

Module 1: Introduction

Lecture 1 Introduction

Definitions of AI

Lecture 2 Broad Classification of AI

Weak/Strong AI, Schools of Thoughts

Lecture 3 Acting humanly: The Turing Test approach

Discussion on "Is Human behavior Rational" and Acting humanly: The Turing Test approach

Lecture 4 Turing's Contribution to AI

Discussion on contributions of Alan Turing, especially objections to AI

Lecture 5 Thinking Humanly and Thinking rationally

Discussion on Thinking Humanly: Cognitive Approach, Thinking rationally: “laws of thought”

Lecture 6 Acting Rationally: Rational Agent Approach

Discussion on Acting rationally: Rational agent approach and its choice as preferred approach.

Lecture 7 Foundations of AI : part I

Discussion on Domains that contributed to AI such as Philosophy, Mathematics, Economics

Lecture 8 Foundations of AI : part II

Discussion on Domains that contributed to AI such as Neuroscience, Psychology, Computer Engineering.

Lecture 9 Foundations of AI : part III

Discussion on Domains that contributed to AI such as Control theory and Linguistics

Lecture 10 Building an AI Machine: part I

Consider what might be involved in building a “smart” computer: Hardware, Software

Lecture 11 Building an AI machine: part II

Consider what might be involved in building a “smart” computer: Game Playing, Speaking, Learning.

Lecture 12 Building an AI machine: part III

Consider what might be involved in building a “smart” computer: Seeing, Planning and Decision Making

Lecture 13 History of AI

Abridged history of AI

Lecture 14 State of the Art

Current status of AI and its fields

Module 2: Intelligent Agents

Lecture 15 Background

Discussion on : Rational Agent is preferred AI approach

Lecture 16 Agents

Definitions of Agent and allied concepts

Lecture 17 Agent Program : Definition

Definition of Agent program with an example of Vacuum cleaner

Lecture 18 Agent Program: Pseudo-code

Pseudo-code for simple Agent Program

Lecture 19 Good Behavior: Rationality

Discussion on Concept of Rationality

Lecture 20 Rational Agents : Choices: part I

Discussion on various of choices for being Rational: Best ? Optimal ? Omniscience ? Clairvoyant ?

Lecture 21 Rational Agents : Choices: part II

Discussion on various of choices for being Rational: Rational ≠ Successful

Lecture 22 Environment Types: part I

Discussion on type of environments

Lecture 23 Environment Types: part II

Discussion on type of environments

Lecture 24 Performance Measure Criterion

Discussion on how to determine if Agent is doing good

Lecture 25 Performance Measure: Vacuum Cleaner

Discussion on how to determine if Agent is doing good, with an example of Vacuum cleaner

Lecture 26 Structure of Agents

Discussion on Basic Agent Program Steps

Lecture 27 Agent Types: part I

Discussion on Agent Types : Simple Reflex Agents, Model-based Reflex Agents

Lecture 28 Agent Types: part II

Discussion on Agent Types: Goal-based Reflex Agents

Lecture 29 Agent Types: part III

Discussion on Agent Types: Utility-based Reflex Agents

Lecture 30 Working of Agent Programs

Discussion on How the Components of Agent Programs Work

Module 3: Key Concepts of AI

Lecture 31 Problem Solving: part I

Discussion on Problem Solving Agent

Lecture 32 Problem Solving: part II

Discussion on Problem Solving Agent with an Example: Travelling in Romania

Lecture 33 Knowledge-Based Agent

Discussion on a Simple Knowledge-Based Agent

Lecture 34 Learning : part I

Discussion on Learning Agents (Machine Learning)

Lecture 35 Learning : part II

Discussion on various types of Learning

Module 4: AI: Present and Future

Lecture 36 Modern AI

Discussion on current trends in AI

Lecture 37 Future AI

Discussion on issues like Technological Singularity, Ethics, etc

Module 5: AI Applications

Lecture 38 AI Applications: part I

Discussion on applications of AI in Finance, Robotics, Games, etc

Lecture 39 AI applications: part II and Conclusion

Discussion on applications of AI in Identification Technologies, Speech Recognition, etc

Lecture 40 References

Discussion on resources used to prepare this course