Essential Math for Artificial Intelligence
Master fundamental mathematics required for AI development in this advanced 2-week course from Columbia University. Perfect for building AI foundations.
Master fundamental mathematics required for AI development in this advanced 2-week course from Columbia University. Perfect for building AI foundations.
This comprehensive course provides essential mathematical foundations crucial for artificial intelligence studies. Students will explore key topics including statistics, linear algebra, multivariable calculus, and probability theory. Through self-paced learning and practical assessments, participants develop a solid understanding of mathematical concepts essential for AI applications. The course emphasizes fundamental math functions including recursions, lambda expressions, and higher-order functions. Designed for those with programming experience, this course serves as a crucial stepping stone for advanced AI coursework, ensuring students have the mathematical proficiency needed for complex AI applications.
Instructors:
English
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What you'll learn
Master essential mathematical concepts required for AI development
Understand applications of statistics and probability in AI
Develop proficiency in linear algebra and multivariable calculus
Learn to work with recursions and lambda expressions
Gain expertise in higher-order functions for AI applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive foundation in the mathematical principles essential for artificial intelligence. The curriculum covers critical areas including statistics, linear algebra, multivariable calculus, and probability theory. Students learn about fundamental mathematical concepts and their applications in AI, including recursions, lambda expressions, and higher-order functions. Through self-paced learning and practical assessments, participants develop the mathematical expertise necessary for advanced AI studies. The course combines theoretical understanding with practical application, ensuring students build the quantitative skills needed for success in AI development.
Fee Structure
Instructor
4 Courses
Expert in Natural Language Processing at Columbia University
Dr. Daniel Bauer is a Senior Lecturer in the discipline of natural language processing in the Department of Computer Science at Columbia University. His research focuses on developing systems that can accurately interpret natural language in multimodal environments and linguistic discourse, with a particular emphasis on the semantics of natural language and efficient translation between surface text, syntax, and semantics. Bauer's work utilizes deep, formal representations of language meaning, applicable to areas like machine translation and natural language user interfaces. With degrees in cognitive science from the University of Osnabrück, language science and technology from Saarland University, and a PhD in computer science from Columbia University, Bauer brings a multidisciplinary approach to his research. His expertise lies in syntactic and semantic parsing, combining machine learning techniques with linguistically inspired formal models. Bauer's innovative work includes using hyperedge replacement graph grammars to construct meaning representations and exploring language grounded in other modalities like formal knowledge bases, 3-D scenes, vision, and robotics. Through his research and teaching, Bauer contributes significantly to advancing natural language processing systems and their applications in various fields
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