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Instructor
Halim Abbas, Instructor - Automatic Planning and Decision Making

Halim Abbas

Has 20-year high tech experience in systems engineering, software development, big data, data science, and artificial intelligence. With Systems Engineering and Computer Science degrees, Halim has enterprise and startup experience with high clientele includes eBay, Nortel, Teradata, ThinkBig, SearchMe, Quixey, Cognoa. Cross-vertical market experience in Search, eCommerce, Telecom, Airline, Online Advertising, Pharma and Healthcare.

Instructor: Halim Abbas

Learn how to build AI agents capable of choosing the best course of action.

  • Learn and understand the foundational concepts and terms related to automated planning and decision making.
  • Build simple decision-making agents leveraging a suite of commonly used AI algorithms.
  • Instructor is a high tech innovator who spearheaded world-class data science projects at game-changing tech firms such as eBay, Teradata, and Quixey.

Course Description

AI automated decision making agents are taking the world by storm, from NASA extraterrestrial robots exploring distant planets, to self-driving cars charting routes and avoiding road hazards, medical decision support systems making diagnostic and treatment recommendations, virtual agents scheming against human players in computer games, and automated day-traders making stock exchange orders in split seconds. This introductory course offers a window into a the emerging field of AI automated agents, the most common algorithms used to build and improve them, and some of the confounding factors and challenges that make this growing science exciting and interesting. Whether you are a hi-tech professional looking to expand your skills, a computer science enthusiast curious about this topic, or a business executive interested in gaining a technical perspective, this course serves as a good starting point for your learning journey.

What am I going to get from this course?

  • Understand the foundational concepts and terms related to automated planning and decision making
  • Build simple decision-making agents leveraging a suite of commonly used AI algorithms
  • Grow and expand by following the latest publications, talks and breakthroughs

Prerequisites and Target Audience

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

  • Familiarity with computer science or computer engineering basics
  • College-level mathematics
  • Basic programming skills (needed only for programming tutorials/challenges)

Who should take this course? Who should not?

Computer science professionals, executives, and enthusiasts who are curious about and unfamiliar with automated planning and decision making.

Curriculum

Module 1: Automatic Planning and Decision Making

Lecture 1 Course Introduction

A brief introduction to the course material, structure, goals, and intended audience.

Lecture 2 Motivation

Why learn about automated planning and decision making?

Module 2: Planning in a Simplified World

Lecture 3 Module Introduction

Simplifying assumptions, and modeling abstractions helpful to frame the planning problem for easy analysis

Lecture 4 Basic Decision Theory

Formal mathematical formulation to the planning problem

Lecture 5 Basic Decision Theory Example

A navigation example from daily life to help explain the mathematical formulations of decision theory.

Lecture 6 State Space Search

A methodical way to explore decision options and arrive at workable action plans

Lecture 7 Uninformed State Space Search

Using Sudoku puzzles to illustrate the power of multiple state-space search algorithms to solve complex planning problems

Quiz 1 Tutorial 1: Sudoku Solver

Hands on session exploring the code for a Sudoku solving AI agent

Lecture 8 Informed State Space Search

An overview of state-space search algorithms that employ pruning and heuristics to find optimal plans faster

Lecture 9 Frugal State Space Search

An overview of cost-aware state-space search algorithms like A*, used to solve path-navigation problems etc.

Quiz 2 Tutorial 2: Maze Solver

Hands on session exploring the code for a maze navigation solving AI agent

Module 3: Planning in the Real World

Lecture 10 Module Introduction

Simplified vs real world problems

Lecture 11 Planning Graphs

An analytical approach to modeling planning problems that involve concurrent actions and non-trivial action dependencies

Lecture 12 Planning Graph Example

A robotics example from to help explain the concept and utility of planning graphs

Lecture 13 GraphPlan Algorithm

A general overview of GraphPlan algorithm using robotics as a working example

Lecture 14 Statistical Decision Theory

A generalization of the basic mathematical formulations explored in Module 2, expanded to include world models that incorporate uncertainty

Lecture 15 Statistical Decision Theory Example

A navigation example from daily life to help explain the mathematical formulations of statistical decision theory

Lecture 16 Markov Decision Processes

An analytical approach to modeling decision planning problems that incorporates aspects of the world's inherent uncertainty

Lecture 17 MDP Policy Extraction

A TV-game show example to help develop an algorithm for using Markov Decision Processes to arrive at optimal plans

Quiz 3 Tutorial 3: Who Wants to be a Millionnaire Planner

Hands on session exploring the code for an AI agent to find optimal strategies for a popular TV game show

Lecture 18 Reinforcement Learning

An overview of an increasingly popular approach to model decision making in the context of obscure knowlege and inherent world ambiguity

Lecture 19 Let's Play Blackjack

A card game example to help explain the central concepts of reinforcement learning

Quiz 4 Blackjack Player

Hands on session exploring the code for an AI-based blackjack player

Module 4: Course Summary

Lecture 20 Course Summary

A summary of the key learning and takeaway from this course, further reading, and recommended next steps.

Reviews

2 Reviews

Small 29c7e769 c80a 46f2 b929 68ef1d1ebc1c
Sajeda M

July, 2019

The course was very helpful to understand the concept of the Automatic Planning and Decision Making and how to make the machine a decision maker.

Small 29c7e769 c80a 46f2 b929 68ef1d1ebc1c
Sajeda M

July, 2019

The course was very helpful to understand the concept of the Automatic Planning and Decision Making and how to make the machine a decision maker.

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