- Course type: Self-paced
- Available Lessons: 36
- Average Lesson Length: 8 min
- New lessons are still being added
Watch a preview:chapter 1 / lesson 1What is Artificial Intelligence? - Definition & History
Course SummaryComputer Science 311 offers a thorough overview of artificial intelligence concepts. Through a series of fun lessons and quizzes, you'll quickly become familiar with fundamental AI topics, You can study the course at any time to improve your AI knowledge for school, work or independent study.
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About This Course
Take Computer Science 311 to learn about the foundations and applications of artificial intelligence (AI). Taught by expert instructors, the course is comprised of short lessons that explore the basics of intelligent agents, constraint satisfaction, logical agents, first-order logic, learning, reasoning and AI trends.
The course can benefit students, professionals and independent learners who want to develop a high-level knowledge of AI. Each lesson comes with a short quiz to help you retain your knowledge, and the course comes equipped with comprehensive chapter exams and a final assessment. You can also access this course at any time that fits your schedule using your computer or mobile device.
|Fundamentals of Artificial Intelligence||Summarize the history of artificial intelligence; recognize several types of AI; identify and evaluate algorithms; define learnable intelligence and the cognitive complexity concept; interpret and identify machine code.|
|Intelligent Agents||Discuss several types of intelligent agents, including simple, model-based, goal-based, utility-based and learning agents.|
|Using Artificial Intelligence in Searches||Write pseudocode and AI programs; calculate uncertainty in scientific applications; explain the Bayes' theorem; use the heuristic method to optimize completeness, accuracy and precision in AI searches; perform an adversarial or uninformed search using algorithms; identify security risks.|
|Constraint Satisfaction in Artificial Intelligence||Complete constraint satisfaction problems; determine how Bayes networks create cognitive thinking in AI applications; describe the different types of neural networks and their uses; analyze data mining and decision tree search techniques; identify Markov decision processes; find hidden Markov models; discuss the applications of SLAM and LISP; implement neural network models.|
|Logical Agents & First-Order Logic||Calculate first-order predicate logic; modify logical agents; explain forward and backward chaining.|
|Learning & Reasoning in Artificial Intelligence||Identify SVMs associated with learning algorithms; improve intelligent program performance; distinguish between supervised and unsupervised machine learning; recognize and modify predictive algorithms; pinpoint details of deep learning in computer programs; explain the basics of image classifications; discuss natural language processing in AI.|
|The Present & Future of Artificial Intelligence||Identify and raise ethical questions about machines; discuss the application of artificial intelligence in non-scientific contexts; recognize the impact of artificial intelligence in the healthcare, manufacturing and other industries; explain how to optimize AI for rapid, cheap and mass production; classify the ways in which we use AI today and where the potential for AI continues to grow; recognize how AI-enabled applications will develop the future; explain how AI has affected the world economy now and what impact it could have on the future.|
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