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Statistics for Artificial Intelligence for Non-Majors

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

5 learners are taking this course

Level Beginner

Course period Unlimited

  • arigaram
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확률
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비전공자
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AI
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AI
AI

What you will gain after the course

  • Understanding that AI is a probabilistic judgment tool

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

  • An attitude that complements AI's limitations by considering data bias and uncertainty

Statistics for Artificial Intelligence for Non-Majors

  • January 13, 2026

    • I've published the lecture for the first time. I've posted 3 lesson videos for now.

    • The course is still being developed.

Statistics, which is full of all kinds of advanced mathematics, is not easy to understand. Therefore, it's best to focus on understanding 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. To understand the principles of how modern artificial intelligence (AI) works and key statistical concepts in an easy and intuitive way,

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

🛠️ Features

  • Learn concept-focused without formulas and proofs

  • Explanations focused on visual materials, 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 AI Thinking
  • Understanding that AI is not a thinking entity, but a tool that makes probabilistic judgments based on data

2. Building Basic Statistical Intuition
  • Acquire essential statistical concepts for understanding AI, such as mean, median, variance, standard deviation, probability, and conditional probability

3. Improving Data Literacy
  • Understanding data structures, variables, samples, and the imperfection of real-world data

  • Experience firsthand that having more data isn't always better

4. Ability to Interpret AI Results
  • Recognizing uncertainty in predictions, overfitting, and data bias

  • Understanding the social and ethical issues that can arise from using AI results as-is

5. AI Utilization Literacy
  • Build foundational competencies that enable even non-specialists to safely use AI as a reference decision-making tool

🛠️ Target Audience

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

  • Beginner level that can be learned even without familiarity with math or statistics

  • Those who want to develop basic competencies to evaluate and utilize AI results

🛠️ Course Features

  • Level: Beginner

  • Target audience: Non-majors

  • Duration: Approximately 10 minutes per lesson, 56 total lectures, approximately 10 hours total

  • Strengths: Intuitive understanding without mathematical formulas

We don't provide fancy charts like drawings on a blackboard in the beginning. At some point... gradually...

🛠️Learning Outcomes

  1. Understanding that AI is a probabilistic decision-making tool,

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

  3. You can make responsible decisions by considering data bias and uncertainty.

🛠️Recommended Next Steps After Learning

  • Deepen your statistical knowledge: AI statistics courses prepared by the instructor for planners/developers/machine learning engineers

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

  • Data Analysis Practice: Python, Excel, Visualization

  • Understanding AI Ethics and Social Impact: Examining Bias, Discrimination, and Policy Implementation

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

Recommended for
these people

Who is this course right for?

  • Someone who felt that existing explanations about artificial intelligence were vague and abstract

  • A person who feels limited in expanding their knowledge about artificial intelligence

  • People who want to gain a deeper understanding of the fundamental principles that run through the foundation of artificial intelligence

Need to know before starting?

  • Artificial Intelligence

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56 lectures ∙ (3hr 39min)

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