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Instructor
Briana Brownell, Instructor - Problem Solving with Artificial Intelligence

Briana Brownell

Founder and CEO at Pure Strategy Inc. She is a Futurist, Tech Entrepreneur, Data Scientist, Speaker, Author.

Instructor: Briana Brownell

Students will learn problem-solving techniques using AI search factors.

Understand the basic framework of artificial intelligence systems used today focusing on the application search methodologies to solve difficult problems.

Instructor is a Founder and CEO at Pure Strategy Inc. She is a Futurist, Tech Entrepreneur, Data Scientist, Speaker, Author.

Duration: 3h 27m

Course Description

Understand the basic framework of artificial intelligence systems used today focusing on the application search methodologies to solve difficult problems. Learn how to solve problems using factors such as searching, informed search, Adversarial Search and Games, Constraint Satisfaction, Local Search, and Applications.

What am I going to get from this course?

Understand the basic framework of artificial intelligence systems used today focusing on the application search methodologies to solve difficult problems.

Prerequisites and Target Audience

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

Students should have basic knowledge of data summarization and decision systems. No coding is necessary for successful completion of the course.

Who should take this course? Who should not?

 Business leaders and professionals who want an introduction to the concepts of artificial intelligence and how AI systems work.

Curriculum

Module 1: Problem Solving by Searching

Lecture 1 Agents and Environments

Thinking about building an application using the concepts of agents and environments provides a way to create any artificial intelligence application. This section provides an overview of the basic concepts that underlie every artificial intelligence system.

Lecture 2 Rationality and Omniscience

We review some of the common assumptions about the actions of agents, including what they know and how they make decisions. We find that combining simple agents together in a larger system can bring us to true intelligence - in both biological and technological systems.

Lecture 3 States and Actions

In this section we introduce Builder, one of the first robotic artificial intelligences created. Following this, we review the relationship between how living things act in their environment and how internal states can lead to sophisticated decision-making processes that solve problems.

Lecture 4 Introduction to Searching

We introduce the concept of search, a way to contemplate potential future actions, within an intelligent system.

Lecture 5 Uninformed Search

We first start with uninformed search: strategies for decision-making with no additional information.

Lecture 6 Practical Considerations of Problem Solving

We wrap up this module talking about some of the practical considerations of the methods that we have reviewed in the course so far.

Module 2: Informed Search

Lecture 7 Best-first Search

We start with best-first search and discuss why it is more effective compared to other search methods we’ve seen so far.

Lecture 8 Dijkstra's Algorithm

In this section we take a deep dive into Dijkstra’s algorithm to build our experience implementing an intelligent strategy to solve a simple problem.

Lecture 9 A* and AO*

We review two of the most important search algorithms, A* and AO*.

Lecture 10 Heuristics

We expand our view of heuristics and include machine learning as a way to learn a heuristic from data.

Lecture 11 Meet Shakey the Robot

Shakey the Robot is one of the most interesting artificial intelligences created. We introduce Shakey and its environment and learn how this robot solved problems using the techniques in this section and how many of these techniques are still used today in self-driving cars and in the Mars Rover, Curiosity.

Module 3: Adversarial Search and Games

Lecture 12 Competition

We begin by looking at the theory of competition and formalize what we mean by strategy, gameplay and winning a game.

Lecture 13 Perfect and Imperfect Information

We take a closer look at games with perfect information, such as chess or tic-tac-toe, and games with imperfect information such as poker. How can we create a strategy in each of these cases?

Lecture 14 Decision-making

Games can become very complicated and cumbersome to analyse. In this section we discuss alpha-beta pruning - a technique to simplify our analysis.

Lecture 15 Chance in Games

Chance introduces another consideration to playing games. We discuss optimal strategy in games that include a chance element such as backgammon.

Lecture 16 Practical Considerations of Competition

Finally, we review some of the practical considerations of games and creating artificial intelligence to play them.

Module 4: Constraint Satisfaction

Lecture 17 Setting up Constraints

We start by exploring ways to set up constraints that can eliminate part of our search space.

Lecture 18 Using Constraints to Solve Problems

Now that we have our constraints defined, we’ll review how to use them to solve problems like the map coloring problem.

Module 5: Local Search    

Lecture 19 Solutions and Paths

We start by looking at the kinds of problems where the path to the solution doesn’t matter and introduce local search.

Lecture 20 Local Optima

Local optima can cause many different problems. We investigate ways to avoid local optima including simulated annealing and local beam search.

Lecture 21 Evolutionary Algorithms

Evolutionary algorithms are based on ideas in biological science. We review ways that these algorithms can be applied to problems in artificial intelligence.

Module 6: Applications in Games, Robotics, Diagnosis and Surveillance

Lecture 22 Checkers and Solving Games

What does it mean to solve a game? We take a deep dive into checkers, one of the first problems solved in part by machine learning.

Lecture 23 Chess and Chess Puzzles

In artificial intelligence, chess has always been a major field of study. In this section, we review some of the historical approaches to AI chess including Mechanical Turk, El Ajedrecista and Deep Blue.

Lecture 24 Poker and Card Games

Games with imperfect information like poker are notoriously challenging. In this section we review the technology that resulted in the poker-playing AI’s Cepheus and Deepstack.

Lecture 25 Robotics

Robotics and Artificial Intelligence is closely linked. We review some of the most interesting robotics projects in AI.

Lecture 26 Business Applications

Artificial intelligence is an extremely important topic in the business world. This section covers several important applications including the Autonomous Vehicles, Scheduling, and Speech Recognition.

Lecture 27 Final Thoughts

This final section sums up the concepts we’ve covered in the course with a short send-off.

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