Artificial Intelligence (AI) from edX

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

What you'll learn

Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine Learning algorithms
Applications of AI (Natural Language Processing, Robotics/Vision)
Solving real AI problems through programming with Python

What's inside

Learning objectives

Introduction to artificial intelligence and intelligent agents, history of artificial intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine learning algorithms

Applications of ai (natural language processing, robotics/vision)
Solving real ai problems through programming with python

Introduction to artificial intelligence and intelligent agents, history of artificial intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine learning algorithms
Applications of ai (natural language processing, robotics/vision)
Solving real ai problems through programming with python

Syllabus

Week 1: Introduction to AI, history of AI, course logisticsWeek 2: Intelligent agents, uninformed searchWeek 3: Heuristic search, A algorithm__Week 4: Adversarial search, games__Week 5: Constraint Satisfaction Problems__Week 6: Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors__Week 7: Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning__Week 8: Markov decision processes and reinforcement learning__Week 9: Logical Agent, propositional logic and first order logic__Week 10: AI applications (NLP)__Week 11: AI applications (Vision/Robotics)__Week 12: * Review and Conclusion

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to AI and ML with a strong foundation

Emphasizes practical understanding through agent building and problem-solving

Engages learners through hands-on exercises, including game and agent creation

Covers essential AI concepts, including search, games, logic, and constraint satisfaction

Familiarizes learners with various ML algorithms, including linear regression and neural networks

Provides an overview of AI applications in natural language processing, robotics, and vision

Reviews summary

Solid introduction to ai

Learners say this course is a solid introduction to AI with engaging and informative programming assignments. Expect to put effort into challenging assignments. There is no staff support and quiz questions can be tedious, testing reading comprehension rather than concept application. The instructor's speech delivery is sometimes unclear and too fast, but the course content is well-structured. Overall, it's a great course, especially for those with a verified certificate in mind.

Challenging and fun programming assignments.

"The programming assignments were in a way the best part of the course."

"The quizzes are so painful."

"The quizzes I feel are a bit of a pain but the projects you have to do are on point."

"They are the perfect amount of challenging to keep you interested."

Great AI course content.

"This course is extremely interesting and well done in terms of contents and assignments."

"I haven't tried BerkeleyX's one (mentioned above here), but I consider this AI course as the best so far."

"Expect to put a not insignificant amount of effort into the programming assignments which at times are on the challenging side."

TAs are unresponsive and unhelpful.

"The weak part of the course is the TA engagement; they weren't very responsive or helpful."

Instructor speaks unclearly and too fast.

"The speaker is often too fast and not clear enough,"

"Taking right now,not as rigorous and intellectually challenging as the UC Berkeley version"

Quiz questions are tedious and test reading comprehension rather than concept application.

"The quiz questions can be a bit on the tedious side testing reading comprehension skills rather than ability to apply the concepts."

Career center

Learners who complete Artificial Intelligence (AI) will develop knowledge and skills that may be useful to these careers:

Algorithm Engineer

An Algorithm Engineer designs and evaluates novel algorithms, and analyze their performance. AI is all about algorithms. Completing this course will provide you with a strong foundation in AI algorithm design and evaluation.

See salaries and explore the career path for Algorithm Engineer

AI Architect

An AI Architect designs and builds AI solutions for businesses. This is a new and emerging career field. This course provides a solid foundation in AI and its applications.

See salaries and explore the career path for AI Architect

Computer Scientist

A Computer Scientist explores the theoretical foundations of computation and information, and use these ideas to design new and innovative computing technologies. AI is one of the most exciting and rapidly developing areas of computer science. This course will provide you with a foundation in the theory and application of AI.

See salaries and explore the career path for Computer Scientist

Researcher

A Researcher investigates, experiments, and produces new knowledge or inventions. As it pertains to AI, research may involve the creation of new AI algorithms or the application of AI to new fields.

See salaries and explore the career path for Researcher

Operations Research Analyst

An Operations Research Analyst uses advanced analytical techniques, including AI, to help organizations make better decisions. This course provides a strong foundation in the use of AI for operations research.

See salaries and explore the career path for Operations Research Analyst

Machine Learning Engineer

A Machine Learning Engineer uses AI, statistics, and programming to build machine learning models and applications. AI and machine learning are intimately connected. Completing this course will provide you with a foundation in both of these subfields.

See salaries and explore the career path for Machine Learning Engineer

Game Designer

A Game Designer designs, builds, and tests video games. This course includes coverage of AI as it pertains to games, providing you with an introduction to one of the primary technological foundations of modern gaming.

See salaries and explore the career path for Game Designer

Data Analyst

A Data Analyst uses AI, statistics, and programming to clean, transform, and visualize data. AI is increasingly used for data analytics. This course may be helpful if you are interested in learning more about this.

See salaries and explore the career path for Data Analyst

Natural Language Processing Engineer

A Natural Language Processing Engineer uses AI to process human language. This course includes a section on NLP, providing you with an introduction to one of NLP's foundational technologies.

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Business Intelligence Analyst

A Business Intelligence Analyst uses business intelligence software and AI to analyze data. AI is increasingly used by BI analysts to perform analysis, generate insights, and provide recommendations.

See salaries and explore the career path for Business Intelligence Analyst

Computer Vision Engineer

A Computer Vision Engineer uses AI to allow computers to see and understand digital images and videos. This course includes a section on Computer Vision, providing you with an introduction to one of CV's foundational technologies.

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Data Scientist

A Data Scientist uses AI, statistics, and programming to extract information and insights from data. AI is the core of data science. This course will provide a solid foundation in AI and its applications. The course also covers fundamental programming concepts that are frequently used by Data Scientists.

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Quantitative Analyst

A Quantitative Analyst builds mathematical and statistical models to solve complex business problems. AI is increasingly becoming integrated into quantitative finance. This course will help you understand the use of AI in this field.

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Robotics Engineer

A Robotics Engineer designs, constructs, and operates robots. AI is used in the construction and operation of robots. This course provides an introduction to the use of AI in robotics.

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Software Engineer

A Software Engineer designs, builds, programs, tests, and maintains computer software. AI is increasingly becoming integrated into software. This course may be particularly helpful if you are interested in constructing AI-powered software applications.

See salaries and explore the career path for Software Engineer

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Artificial Intelligence (AI)

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Reviews summary

Solid introduction to ai

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