Mathematics courses for programmers strengthen basic development skills and use mathematical principles to optimize code

Programmers' mathematics class consolidates basic development skills and uses mathematical principles to optimize code resources:

Course Background

Programming statements, data structures, and algorithms, these basic skills of developers are all built on the basis of mathematics. The educational threshold for recruiting people in large companies is essentially a moat for building underlying capabilities and development potential. In daily development work, the logic of problem solving and code optimization methods all reflect the importance of mathematical thinking.

Column Interpretation

For programmers, it is not necessary to learn mathematical theories in an extensive and comprehensive manner. The core goal of this column is to streamline the mathematical knowledge that programmers can use, feed mathematical principles into practice, cultivate mathematical thinking, and improve problem-solving skills.

This column has 23 lectures in total. Through the following 4 modules, you can become a first-class programmer who understands mathematics:

① Develop ubiquitous mathematical thinking. This module explains how mathematical thinking is used in daily programming work through principles such as number system conversion, mathematical logic, classic formulas, and business code examples, allowing you to re-examine the role of mathematical thinking in your work.

② Essential mathematical principles for programmers: algebra and statistics. Concise mathematical knowledge, such as finding extreme values, vectors and derivatives, and how these are applied in practical work: how to find the optimal solution for complex business, and how to complete massive high-dimensional data calculations. This module will introduce you to the practical application of mathematical principles and lay a solid theoretical foundation for you.

③ Lay a solid foundation in programming: algorithms and data structures. This module will discuss common algorithms and important ideas such as binary search, dynamic programming, and recursion, find the rules behind the algorithms from a mathematical perspective, and combine them with actual scenarios, such as using exponential explosion optimization programs, combining mathematical principles, algorithms, and actual combat to consolidate basic development skills with half the effort.

④ AI and machine learning, core technologies in popular fields. The technical core of AI and machine learning is actually mathematical problems. This module will introduce you to AI modeling through several common technical points, such as logistic regression and decision trees, so that you can understand current hot fields from a mathematical perspective.

Lecturer Introduction

Gongjin, PhD from Chinese Academy of Sciences, senior algorithm expert

A senior algorithm expert at a domestic first-tier Internet company, one of the first engineers in China to engage in machine learning and data mining. He has been engaged in algorithm work in the industry for more than 5 years. He has profound first-line code development experience and has many years of research and in-depth understanding of data structures and algorithmic thinking.

Course Catalog

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Opening words: mathematics, the nutritional foundation of programming ability.mp4

01 Starting from counting, programmers must know the number system conversion method.mp4

02 Logic and communication, how can we speak logically?.mp4

03 How to plan input, conversion and output using mathematical decision making?.mp4

04 Everything can be mathematical, how are classic formulas applied in life?.mp4

05 Seeking extreme values: How to find the optimal solution for complex business?.mp4

06 Vectors and their derivatives: How do computers complete calculations on massive high-dimensional data?.mp4

07 Linear regression: How to find data patterns in discrete points?.mp4

08 Addition and Multiplication Rule: How to calculate the probability of complex events?.mp4

09 Likelihood Estimation: How to use MLE to estimate parameters?.mp4

10 Information Entropy: How to Calculate the Uncertainty of an Event?.mp4

11 Grayscale Experiment: How to design a grayscale experiment and calculate the benefits of the experiment?.mp4

12 Statistical methods: How to prove that the grayscale experimental effect is not obtained by chance?.mp4

13 Complexity: How to use mathematical derivation to optimize the program?.mp4

14 Program loop: How to use mathematical induction to develop programs?.mp4

15 Recursion: How to calculate the number of moves in the Tower of Hanoi problem?.mp4

16 Binary Method: How to use exponential explosion to optimize the program?.mp4

17 Dynamic Programming: How to use optimal substructure to solve problems?.mp4

18 AI Introduction: Using 3 formulas to build the simplest AI framework.mp4

19 Logistic regression: How to make computers make binary decisions?.mp4

20 Decision Tree: How to heuristically solve NP-hard complex problems?.mp4

21 Neural Networks and Deep Learning: How do computers understand images, text, and speech?.mp4

22 Algorithm questions that have trapped countless people in interviews.mp4

23 Standing at the crossroads of life, how to use mathematics to make decisions?.mp4

Conclusion: With a good foundation in mathematics, you can learn anything quickly.mp4