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  KAIST 산업및시스템공학과 문일철 교수

  KAIST 산업및시스템공학과 교수
  KAIST 김재철AI대학원 겸임교수
  KAIST 항공우주공학과 겸임교수
  KAIST 안보융합연구원 겸임교수
  한국인공지능학회 교육이사

1. CHAPTER 1 Data Handling and Descriptive Statistics
   1. 1-1. Value chain of datascience programming
   1. 1-2. Dataset
   1. 1-3. Dataset Read with Pandas
   1. 1-4. Histogram and Bin size selection
   1. 1-5. Descriptive Statistics
   1. 1-6. Unbiased Estimator
   1. 1-7. Matplotlib
   1. 1-8. Pivot Table
   1. Lecture Note Chapter 1
   1. Quiz 1
2. CHAPTER 2 Distribution and Parameter Inference
   1. 2-1. detour - Bin Size Selection and Model Complexity
   1. 2-2. Distribution
   1. 2-3. Normal Distribution
   1. 2-4. Gamma Distribution and Moment Statistics
   1. 2-5. Method of Moments
   1. 2-6. Method of Moments - Gamma Dist
   1. 2-7. Result from Method of Memoments
   1. 2-8. MLE of Gaussian Distribution
   1. 2-9. MLE of Gamma Distribution
   1. 2-10. Parameter Estimation by SciPy
   1. Lecture Note Chapter 2
   1. Quiz 2
3. CHAPTER 3 Confidence Interval and Bootstrapping
   1. 3-1. Sample and Population
   1. 3-2. Point Estimate on Multiple Samples
   1. 3-3. Central Limit Theorem
   1. 3-4. Properties of Expectation
   1. 3-5. Distribution of Sample Statistics
   1. 3-6. Confidence Interval of Normal Distribution
   1. 3-7. Bootstrapping
   1. 3-8. Implementation of Bootstrapping
   1. 3-9. Confidence Interval By SciPy
   1. Lecture Note Chapter 3
   1. Quiz 3
4. CHAPTER 4 Multivariate Distribution and Covariance
   1. 4-1. Multivariate Gaussian Distribution
   1. 4-2. Samples of Multivariate Gaussian Distribution
   1. 4-3. Covariance Matrix
   1. 4-4. Pearson Correlation
   1. 4-5. Scatter Plot Matrix
   1. 4-6. Kernel Density Estimation
   1. 4-7. Correlation Table
   1. Lecture Note Chapter 4
   1. Quiz 4
5. CHAPTER 5 Linear Regression
   1. 5-1. Deterministic Relation
   1. 5-2. Estimation of Dependent Variable
   1. 5-3. Estimating the parameter, beta
   1. 5-4. Parameter Estimation by Least Square
   1. 5-5. Implementation of Simple Linear Regression
   1. 5-6. Evaluation of Linear Regression
   1. 5-7. R-Square
   1. Lecture Note Chapter 5
   1. Quiz 5
6. ★강의 수강 후 의견을 부탁드리겠습니다.★
   1. 교수님 강의에 대한 별점을 매겨주세요. 여러분의 의견이 많은 도움이 됩니다:D

본 강좌는 Python3 를 기반으로 진행되는 강좌 입니다.
기초적인 내용부터 시작하기 때문에, 누구나 수강할 수 있는 강좌입니다.
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