
Artificial Intelligence
About Course
In this course, you’ll learn about the foundations of AI. You’ll configure your programming environment to work on AI problems with Python. At the end of the course you’ll build a Sudoku solver and solve constraint satisfaction problems.eDiploma Credit: 2 Points
This program will teach you how to become
a better Artificial Intelligence or Machine
Learning Engineer by teaching you classical
AI algorithms applied to common problem
types. You will complete projects and exercises
incorporating search, optimization, planning,
and probabilistic graphical models which have
been used in Artificial Intelligence applications
for automation, logistics, operations research,
and more. These concepts form the foundation
for many of the most exciting advances in AI
in recent years. Each project you build will be
an opportunity to demonstrate what you’ve
learned in your lessons, and become part of
a career portfolio that will demonstrate your
mastery of these skills to potential employers.
Course Curriculum
Introduction to Artificial Intelligence
-
WELCOME TO ARTIFICIAL INTELLIGENCE
-
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
-
SOLVING SUDOKU WITH AI
-
SETTING UP YOUR ENVIRONMENT
-
CONSTRAINT SATISFACTION PROBLEMS
Classical Search
-
INTRODUCTION
-
UNINFORMED SEARCH
-
INFORMED SEARCH
-
CLASSROOM EXERCISE: SEARCH
-
ADDITIONAL TOPICS: SEARCH
Automated Planning
-
SYMBOLIC LOGIC & REASONING
-
INTRODUCTION TO AUTOMATED PLANNING
-
CLASSICAL PLANNING
-
ADDITIONAL TOPICS IN PLANNING
Optimization Problems
-
INTRODUCTION
-
HILL CLIMBING
-
SIMULATED ANNEALING
-
GENETIC ALGORITHMS
-
CLASSROOM EXERCISE: OPTIMIZATION PROBLEMS
-
ADDITIONAL OPTIMIZATION TOPICS
Adversarial Search
-
SEARCH IN MULTI-AGENT DOMAINS
-
OPTIMIZING MINIMAX SEARCH
-
EXTENDING MINIMAX SEARCH
-
ADDITIONAL ADVERSARIAL SEARCH TOPICS
Fundamentals of Probabilistic Graphical Models
-
INTRODUCTION TO PROBABILISTIC MODELS
-
PROBABILITY
-
BAYES NETS
-
INFERENCE IN BAYES NETS
Fundamentals of Probabilistic Graphical Models cont.
-
HIDDEN MARKOV MODELS
-
DYNAMIC TIME WARPING
Order Certificate
-
Order your certificate00:00