스터디 일정 & 커리큘럼 & 발표자료

진행 중

회차 일시 내용 발표자 발표자료
1 12/14 (파이썬) 1.Machine learning basics 권준호 발표자료
(파이썬) 2.Classifying with k-Nearest Neighbors 권준호 발표자료
(코세라) 01 Part 1 of 4 Introduction to Regression 김성근
(코세라) 01 Part 2 of 4 Basic Least Squares 김성근
(코세라) 01 Part 3 of 4 Technical Details 김성근
(코세라) 01 Part 4 of 4 Introductory Data Example 김성근
(코세라) 02 Part 1 of 1 Notation and Background 박상진
2 12/21 (파이썬) 3. Splitting datasets one feature at a time: decision trees 신성진 발표자료
(파이썬) 추가발표자료1 신성진 발표자료
(파이썬) 추가발표자료2 신성진 발표자료
(코세라) 03 Part 1 of 3 Linear Least Squares 김성근
(코세라) 03 Part 2 of 3 Linear Least Squares Coding Example 김성근
(코세라) 03 Part 3 of 3 Technical Details (Skip if you'd like) 김성근
(코세라) 04 Part 1 of 1 Regression to the Mean 김성근
3 1/4 (파이썬) 4.Classifying with probability theory: nai:ve Bayes 조정호
(코세라) 05 Part 1 of 3 Statistical Linear Regression Models 김성근
(코세라) 05 Part 2 of 3 Interpreting Coefficients 김성근
(코세라) 05 Part 3 of 3 Linear Regression for Prediction 김성근
(코세라) 01_06 Part 1 of 3 Residuals 서기호
(코세라) 01_06 Part 2 of 3 Residuals, Coding Example 서기호
(코세라) 01_06 Part 3 of 3 Residual Variance 서기호
4 1/11 (파이썬) 5. Logistic regression
(파이썬) 6. Support vector machines
(코세라) 01_07 Part 1 of 3 Inference in Regression
(코세라) 01_07 Part 2 of 3 Coding Example
(코세라) 01_07 Part 3 of 3 Prediction
5 1/18 (파이썬) 7. Improving classification with the AdaBoost
(코세라) 02_01 Part 1 of 3 Multivariable Regression
(코세라) 02_01 Part 2 of 3 Multivariable Regression
(코세라) 02_01 Part 3 of 3 Multivariable Regression Continued
(코세라) 02 02 Part 1 of 4 Multivariable Regression Examples
(코세라) 02 02 Part 2 of 4 Multivariable Regression Examples
(코세라) 02 02 Part 3 of 4 Multivariable Regression Examples
(코세라) 02 02 Part 4 of 4 Multivariable Regression Examples
(코세라) 02 03 Part 1 of 1 Adjustment Examples
6 1/25 (파이썬) 8. Predicting numeric values: regression
(파이썬) 9. Tree-based regression
(코세라) 02 04 Part 1 of 3 Residuals and Diagnostics
(코세라) 02 04 Part 2 of 3 Residuals and Diagnostics
(코세라) 02 04 Part 3 of 3 Residuals and Diagnostics
(코세라) 02 05 Part 1 of 3 Model Selection
(코세라) 02 05 Part 2 of 3 Model Selection
(코세라) 02 05 Part 3 of 3 Model Selection
7 2/1 (파이썬) 10. Grouping unlabeled items using k-means clustering
(파이썬) 11. Association analysis with the Apriori algorithm
(코세라) 03 01 Part 1 of 1 GLMs
(코세라) 03 02 Part 1 of 3 Logistic Regression
(코세라) 03 02 Part 2 of 3 Logistic Regression
(코세라) 03 02 Part 3 of 3 Logistic Regression
8 2/8 (파이썬) 12. Efficiently finding frequent itemsets with FP-growth
(파이썬) 14.SimplifYing data with the singular value decomposition
(코세라) 03 03 Part 1 of 2 Poisson Regression
(코세라) 03 03 Part 1 of 2 Poisson Regression
(코세라) 03 04 Part 1 of 1 Hodgepodge
9 2/15 (파이썬) 13. Using principal component analysis to simplifY data
(파이썬) SimplifYing data with the singular value decomposition
(파이썬) 15. Big data and MapReduce15 • Big data and MapReduce
Written on January 1, 2016