Foundational Mathematics for AI
Coursera
32 Courses
Johns Hopkins University is a globally recognized research university comprising 9 schools and campuses worldwide. It provides more than 260 degree programs, ranging from undergraduate to graduate and postdoctoral studies.
Overview
Embark on a comprehensive journey through the mathematical foundations of artificial intelligence with the "Foundational Mathematics for AI" course. Offered by Johns Hopkins University through Coursera, this course is meticulously designed for learners from diverse academic backgrounds. It connects key mathematical concepts with real-world AI applications, empowering you to employ mathematical techniques critical for AI development.
By the conclusion of this course, you will gain the proficiency to apply functions, matrices, and vectors to effectively represent and analyze data relationships. Master the use of descriptive statistics and visualization techniques to explore and succinctly summarize datasets. Acquire the skills to solve systems of linear equations and model complex relationships using linear regression across single and multiple variables.
Furthermore, delve into foundational principles of probability, including the application of Bayes' Theorem, while advancing to sophisticated mathematical techniques in calculus. Develop your understanding of derivatives and integrals to analyze rates of change and distributions, essential for optimization and modeling within AI contexts.
Explore concepts from linear algebra, utilizing advanced topics like eigenvectors, determinants, and linear transformations to achieve dimensionality reduction and classification algorithms. This specialized curriculum offers mathematical techniques directly applicable to AI and machine learning, effectively bridging the gap between theory and practical application.
Engage with interactive modules, real-world datasets, and tools such as Python and Excel to not merely grasp concepts but also apply them to solve tangible problems. Course modules, including Descriptive Statistics, Linear Algebra, Probability, and Optimization, are structured to bolster your knowledge progressively while linking each idea to practical AI applications.
Whether aiming to model salaries using linear regression or applying optimization tactics in clustering algorithms, gain comprehensive insights into applications of theory. This course not only equips you with mathematical fluency for advanced AI studies like deep learning or natural language processing but also prepares you to contribute meaningfully to the rapidly evolving domain of artificial intelligence.
Ideal for engineers, data scientists, or AI enthusiasts, this course delivers the mathematical grounding necessary for understanding and partaking in AI’s advancing frontier. Join us to master foundational math skills and gain a competitive edge in artificial intelligence.
University: Johns Hopkins University
Provider: Coursera
Categories: Mathematics Courses, Calculus Courses, Linear Algebra Courses, Derivatives Courses, Probability Theory Courses, Matrices Courses, Vectors Courses