AI Statistics for Non-Majors

Without a single formula or line of code, this penetrates the essence of basic statistics necessary for AI development and application.

12 learners are taking this course

Level Beginner

Course period Unlimited

AI
AI
AI
AI

What you will gain after the course

  • Understanding that AI is a probabilistic decision-making tool

  • The ability to interpret AI-generated results based on statistical thinking

  • An attitude of compensating for AI's limitations by considering data bias and uncertainty

AI Statistics for Non-Majors

🧭 Precautions

The course is currently in the process of being completed. Please note that there is a disadvantage in that you may have to wait a long time until the course is fully finished (although I will be adding supplementary materials frequently). Please take this into consideration when making your purchase decision.

📋Change History

  • March 18, 2026

    • The entire course is being revised to the [2nd Edition]. During the revision period, the 2nd edition course materials will be posted first without videos, and the videos will be recorded and uploaded over several days. Therefore, there will be a period of a few days where no videos are available.

  • January 13, 2026

    • The lecture has been posted for the first time. For now, I have posted three lesson videos.

    • The lectures are currently being completed.

It is not easy to understand statistics, where all sorts of advanced mathematics run rampant. Therefore, it is better to focus on understanding the concepts at first without using formulas or code.

🛠️ Course Overview

  1. This course is an introductory program designed for non-majors with little to no background in mathematics or statistics.

  2. Understand the principles of how modern Artificial Intelligence (AI) works and core statistical concepts easily and intuitively,

  3. The goal is to develop the ability to correctly interpret and utilize AI results in practical, policy, and planning tasks.

🛠️ Features

  • Learning focused on concepts without formulas or proofs

  • Explanations focused on visual aids, examples, and analogies

  • Understanding AI's probabilistic judgment, data bias, and uncertainty

  • Including social context and ethical considerations

🛠️ Learning Objectives

Through this course, students will be able to learn the following.

1. Establishing an AI Mindset
  • Understand that AI is not a thinking being, but a tool that makes probabilistic judgments based on data.

2. Developing basic statistical intuition
  • Acquire essential statistical concepts for understanding AI, such as mean, median, variance, standard deviation, probability, and conditional probability

3. Improving data intuition
  • Understanding data structures, variables, samples, and the incompleteness of real-world data

  • Realizing that having a lot of data is not always a good thing

4. AI result interpretation ability
  • Awareness of uncertainty in prediction results, overfitting, and data bias

  • Understanding the social and ethical issues that can arise when using AI results as they are

5. AI Literacy
  • Acquiring foundational competencies so that even non-majors can safely utilize AI as a reference tool for judgment.

🛠️ Target Audience

  • Planners, designers, policy makers, and general office workers who are new to AI

  • An introductory level that can be learned even if you are not familiar with mathematics or statistics.

  • Those who want to acquire the foundational skills to evaluate and utilize AI results.

🛠️ Course Features

  • Level: Introductory

  • Target: Non-majors

  • Time required: Approximately 10 minutes per lesson, 56 lessons in total, approximately 10 hours in total

  • Advantages: Intuitive understanding without formulas

We do not provide flashy diagrams like those drawn on a blackboard in the beginning. When the time comes... gradually... more and more...

🛠️ Learning Effects

  1. Understand that AI is a tool for probabilistic judgment, and

  2. You can interpret AI results based on statistical thinking, and

  3. Responsible decision-making becomes possible by considering data bias and uncertainty.

🛠️ Recommended Next Steps after Learning

  • Deepening Statistical Knowledge: AI Statistics courses prepared by the instructor for planners, developers, and machine learning engineers

  • Learning Machine Learning Basics: Supervised/Unsupervised Learning, Classification/Regression

  • Data Analysis Practice: Python, Excel, Visualization

  • Understanding AI Ethics and Social Impact: Reviewing Bias, Discrimination, and Policy Application

Starting with this course (introductory level for non-majors), which is the first lecture related to statistics required for artificial intelligence, you will be able to gradually progress from the planner level to the developer level, and from the developer level to the machine learning engineer level.

Recommended for
these people

Who is this course right for?

  • Those who felt that existing explanations of AI were too vague and abstract

  • Someone who has felt limited in expanding their knowledge of artificial intelligence

  • Those who want to gain a deeper understanding of the fundamental principles underlying artificial intelligence.

Need to know before starting?

  • Artificial Intelligence

Hello
This is arigaram

691

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38

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2

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4.6

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18

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I am someone for whom IT is both a hobby and a profession.

I have a diverse background in writing, translation, consulting, development, and lecturing.

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56 lectures ∙ (8hr 53min)

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