Exam Date and Time: September 17 (Thursday) / 60 minutes.
Exam Format: Written Exam (Closed Book).
Required Materials: Writing materials and calculator.
Exam Coverage:
- Chapter 2: Summarizing data
- Chapter 3: Probability
- Chapter 4: Random Variables
Exam Guidelines:
- Calculations and solution steps necessary to derive the answer must be included. Writing only the final answer will result in partial credit.
- No discussions are allowed during the exam.
- Any form of academic dishonesty or cheating will be addressed according to the Cadet Life Regulations(생도생활예규).
Welcome to the class: Introduction to Statistics for Artificial Intelligence!
Check out the syllabus and course schedule for some basic information about this class.
If you have any questions, feel free to email me (yhkwon@kma.ac.kr) or reach out via Kakaotalk. I look forward to working with you.
Best wishes,
Yonghyun
This course provides a first introduction to statistical thinking and data analysis, taught in English. Students learn to collect, summarize, and interpret data using probability theory, descriptive statistics, and foundational inferential methods.
Learning Objectives
- Understand fundamental theories and principles of statistics.
- Acquire statistical analysis techniques for scientific problem-solving and optimal decision-making.
- Develop the ability to analyze and interpret data using statistical software (R).
Topics Covered
Ch. 1 Introduction to data · Ch. 2 Summarizing data · Ch. 3 Probability · Ch. 4 Random variables · Ch. 5 Distributions · Ch. 6 Foundations for inference · Ch. 7 Sampling distribution and point estimation · Ch. 8 Inference for means · Ch. 9 Linear regression
Teaching Method
Lecture 80% · Review 10% · Q&A 10%. Lectures use PPT slides and board writing. Online sessions via Zoom when necessary.
Textbook
Kwon, Bang, Oh & Park, Introduction to Statistics for Artificial Intelligence, 1st ed. — PDF
Prerequisites
Calculus.
All sessions are 75 minutes. Quiz = in-class written quiz.
| Wk | Date | Topic | |
|---|---|---|---|
| 1 | Sep 1 (Tue) | Ch. 1 & 2 — Introduction to Data & Summarizing Data Presenting numerical / categorical data | |
| 1 | Sep 3 (Thu) | Ch. 3 — Probability Conditional probability, Bayes' Theorem | |
| 2 | Sep 8 (Tue) | Ch. 4 — Random Variables Random variables, expectation, variance | |
| 2 | Sep 10 (Thu) | Ch. 4 — Random Variables (cont.) Joint pdf, independence, covariance, correlation | |
| 3 | Sep 15 (Tue) | Ch. 5 — Distributions Uniform distribution, Normal distribution | |
| 3 | Sep 17 (Thu) | Quiz 1 | Quiz 1 |
| 4 | Sep 22 (Tue) | Ch. 5 — Distributions (cont.) Bernoulli distribution, Binomial distribution | |
| 4 | Sep 24 (Thu) | Chuseok (Korean Thanksgiving) | No Class |
| 5 | Sep 29 (Tue) | Ch. 6 — Foundations for Inference Point estimates and sampling variability, Central Limit Theorem | |
| 5 | Oct 1 (Thu) | Armed Forces Day | No Class |
| 6 | Oct 6 (Tue) | Ch. 6 — Foundations for Inference (cont.) Confidence intervals for a proportion | |
| 6 | Oct 8 (Thu) | Ch. 7 — Sampling Distribution and Point Estimation Sampling distribution | |
| 7 | Oct 13 (Tue) | Ch. 7 — Sampling Distribution and Point Estimation (cont.) Point estimation | |
| 7 | Oct 15 (Thu) | Hwarang Festival | No Class |
| 8 | Oct 20 (Tue) | Quiz 2 | Quiz 2 |
| 8 | Oct 22 (Thu) | Summary & Review | |
| 9 | Oct 27–29 | Midterm Exam Coverage: Ch. 1–7 | Midterm |
| 10–11 | Nov 3–12 | Joint Training (합동교육) Four sessions: Nov 3, 5, 10, 12 | No Class |
| 12 | Nov 17 (Tue) | Special Lecture Date TBD | |
| 12 | Nov 19 (Thu) | Ch. 8 — Inference for Means One-sample means with the t-distribution (1) | |
| 13 | Nov 24 (Tue) | Ch. 8 — Inference for Means (cont.) One-sample means with the t-distribution (2) | |
| 13 | Nov 26 (Thu) | Ch. 8 — Inference for Means (cont.) Hypothesis test for population mean (1) | |
| 14 | Dec 1 (Tue) | Ch. 8 — Inference for Means (cont.) Hypothesis test for population mean (2) | |
| 14 | Dec 3 (Thu) | Ch. 8 — Inference for Means (cont.) Paired data | |
| 15 | Dec 8 (Tue) | Quiz 3 | Quiz 3 |
| 15 | Dec 10 (Thu) | Ch. 8 — Inference for Means (cont.) Difference of two means (1) | |
| 16 | Dec 15 (Tue) | Ch. 8 — Inference for Means (cont.) Difference of two means (2) | |
| 16 | Dec 17 (Thu) | Ch. 8 — Inference for Means (cont.) Power calculations for a difference of means | |
| 17 | Dec 22 (Tue) | Ch. 9 — Linear Regression Fitting a line, residuals, and correlation (1) | |
| 17 | Dec 24 (Thu) | Ch. 9 — Linear Regression (cont.) Fitting a line, residuals, and correlation (2) | |
| 18 | Dec 29 (Tue) | Quiz 4 | Quiz 4 |
| 18 | Dec 31 (Thu) | Summary & Review | |
| 19 | Jan 5–7, 2027 | Final Exam Coverage: Comprehensive | Final |
Chapters follow the 1st edition of the textbook. The annotated version of each deck is updated as the chapter is covered in class.
| Chapter | Files |
|---|---|
Ch. 1 — Introduction to Data Statistics and AI, data and variables, populations and samples | Slides Annotated |
Ch. 2 — Summarizing Data Examining numerical data, considering categorical data | Slides Annotated |
Ch. 3 — Probability Defining probability, conditional probability | Slides Annotated |
Ch. 4 — Random Variables Defining random variables, expectation and variance, joint distributions | Slides Annotated |
Ch. 5 — Distributions Uniform, Normal, Binomial | Slides Annotated |
Ch. 6 — Foundations for Inference Point estimates and sampling variability, confidence intervals for a proportion | Slides Annotated |
Ch. 7 — Sampling Distribution and Point Estimation Sampling distribution, point estimation | Slides Annotated |
Ch. 8 — Inference for Means Confidence intervals, hypothesis tests, paired data, difference of two means, ANOVA | Slides Annotated |
Ch. 9 — Linear Regression Line fitting and residuals, least squares, inference, multiple regression | Slides Annotated |
Ch. 10 — Logistic Regression Modeling a binary outcome, evaluating decisions, from regression to machine learning | Slides Annotated |
Extra — Inference for Categorical Data Supplementary; not part of the numbered chapters | Slides Annotated |
2026 Fall
| Item | Date | Files |
|---|---|---|
Quiz 1 |
Sep 17 | ProblemSolution |
Quiz 2 |
Oct 20 | ProblemSolution |
Midterm Exam |
Oct 27–29 | ProblemSolution |
Quiz 3 |
Dec 8 | ProblemSolution |
Quiz 4 |
Dec 29 | ProblemSolution |
Final Exam |
Jan 5–7, 2027 | ProblemSolution |
Problems and solutions are posted after each assessment.
2025 Spring
| Item | Files |
|---|---|
Quiz 1 |
ProblemSolution |
Quiz 2 |
ProblemSolution |
Midterm Exam |
ProblemSolution |
Quiz 3 |
ProblemSolution |
Quiz 4 |
ProblemSolution |
Final Exam |
ProblemSolution |
2025 Fall
| Item | Files |
|---|---|
Quiz 1 |
ProblemSolution |
Quiz 2 |
ProblemSolution |
Midterm Exam |
ProblemSolution |
Quiz 3 |
ProblemSolution |
Quiz 4 |
ProblemSolution |
Final Exam |
ProblemSolution |
2026 Spring
| Item | Files |
|---|---|
Quiz 1 |
ProblemSolution |
Quiz 2 |
ProblemSolution |
Midterm Exam |
ProblemSolution |
Quiz 3 |
ProblemSolution |
Quiz 4 |
ProblemSolutionReference |
Final Exam |
ProblemSolutionReference |
Due dates are announced in class.
| Homework | Coverage | Files |
|---|---|---|
HW 1 | Ch. 2 — Summarizing data (box plots, IQR) | ProblemSolution |
HW 2 | Ch. 3–4 — Probability models, expectation and variance | ProblemSolution |
HW 3 | Ch. 5 — Normal distribution | ProblemSolution |
HW 4 | Ch. 5–6 — Uniform distribution, Central Limit Theorem | ProblemSolution |
HW 5 | Ch. 7 — Point estimation, unbiased estimators | ProblemSolution |
HW 6 | Ch. 8 — Confidence interval for a population mean | ProblemSolution |
HW 7 | Ch. 8 — One-sample means with the t-distribution | ProblemSolution |
HW 8 | Ch. 8 — Hypothesis test for a population mean | ProblemSolution |
HW 9 | Ch. 8 — Difference of two means | ProblemSolution |
HW 10 | Ch. 9 — Line fitting, residuals, and correlation | ProblemSolution |
HW 11 | Ch. 9 — Inference for linear regression | ProblemSolution |
