인프런 - 라이프타임 커리어 플랫폼

This lecture is designed to provide a foundation in middle and high school mathematics for adults who gave up on math during their school years but are now looking to tackle engineering mathematics.

Attention to those who want to study stocks! But, you are studying stocks without knowing financial engineering? How about learning how to make money properly through financial engineering from the basics?

In this course, you will learn the basics of optimization theory for multivariable functions and acquire a general understanding of vector functions.

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

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

Were you a math-phobe during your school days? It's said that math is important in AI, but do you feel lost on how to approach it? The reason math is important in AI is because math is used in the computational processes of AI (Machine Learning and Deep Learning). For easy explanations of differentiation, linear algebra, and probability and statistics to understand AI, it is structured with simple explanations using graphs (coordinates) rather than mathematical derivations.

This course will provide students with a solid foundation in the fundamentals of differentiation in vector calculus (Calculus 2).

If calculus is too difficult? From the basics, step by step! Covers university-level calculus.

You can understand the theoretical operation principle of the Kalman Filter through easy examples.

This lecture is a lecture that only extracts the core of set theory. Starting with this lecture, you can start various mathematics such as linear algebra and calculus without any difficulty.

This course will help you build your knowledge of advanced integration of multivariable functions.

For those who are interested in game development and know that math is important, but don't know what math to study, I've prepared a lecture that provides a general explanation. I want to talk about the various maths used in the game production process.

Let's build a foundation of MATLAB and apply and understand the basic concepts of programming!

Dynamic Programming That's Untouchable, From Math to Key Elements to Look for in Problems, Complete Summary from Basics to Practice

How should we integrate the results of different diagnostic methods when they are different? Are there any diagnostic methods that are unnecessary among the various diagnostic methods? We will provide answers to these questions.

Learn why Propensity Score Matching analysis is so popular and what its advantages are. Also, use it to easily create tables and graphs that can be used immediately when submitting a paper.

This course is for those who want to study machine learning but lack statistical knowledge.

For those of you who are having a hard time with college statistics, a senior who was in the same position will share his know-how. Conquering basic statistics using R programming

The core of all AI/deep learning is ultimately optimization. Of course, optimization theory is not only needed in AI/deep learning. Computer vision, robotics & control systems, financial engineering & quant, data science, signal processing... Probably no matter which field you study, optimization theory is one of the topics you will inevitably encounter when you go deeper. This lecture systematically covers everything from basic mathematical theory to in-depth content. I believe it is by far the best optimization theory lecture offered in Korea.

This course covers university-level first-year calculus. We focus intensively on single-variable function differentiation and learn various differentiation methods and formulas. As practical applications of differentiation, we select and study cases such as linear approximation, function increase and decrease, and optimization problems.