인프런 - 라이프타임 커리어 플랫폼

You can understand the theoretical operating principles of the Kalman Filter through easy examples.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

Anyone who likes math, raise your hand! Anyone who wants to learn probability and statistics, raise your hand!

This course delves into the essential poker and mathematical concepts necessary for mastering Texas Hold'em at a professional level. Learners will explore strategies and theories that elevate their gameplay and decision-making skills.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

This course covers key topics in discrete applied mathematics, focusing on counting, generating functions, probability, and more. Learners will engage with both mathematical concepts and effective communication through writing assignments.

In this course, you will learn the mathematical principles and intuition of discrete stochastic processes and master how to create and analyze various models. You will explore discrete probability models that are useful in various fields such as engineering, physics, and biology.

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

This course covers classical and modern developments in graph theory and additive combinatorics, with a focus on topics and themes that connect the two subjects. It also includes an introduction to current research topics and open problems.

This course provides an interactive introduction to discrete mathematics for computer science and engineering. Learners will study fundamental mathematical concepts, discrete structures, and discrete probability theory, developing the ability to apply these to various fields of computer science.

This course is a graduate-level introduction to the probabilistic methods, a fundamental and powerful technique in combinatorics and theoretical computer science. Learners will explore how to demonstrate the existence of combinatorial objects and validate random constructions with positive probability.

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is essential to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence!

This course covers fundamental discrete mathematics for computer science and engineering. It focuses on mathematical definitions, proofs, and applicable methods, covering various topics such as logic symbols, proof techniques, sets, relations, and graph theory.

This course covers the tools of probability theory and statistical inference, providing essential content for data analysis and understanding. Learners can acquire methods for utilizing data in various fields.

Learn Bayesian Inference, a core methodology of statistical reasoning, and the Hayashi Quantification Method specialized for categorical data analysis. Using SPSS Bayesian Statistics, you will practice various statistical analyses including binomial proportions, mean differences, correlation coefficients, regression models, and ANOVA, while mastering Quantification Methods 1–4 to derive internal insights from categorical variables.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.