machine-learning-lecture-notes

Reading material
- 추정 이론 : 추정 이론에 대한 수리통계학 강의자료 입니다.
- 표본 분포 이론 : 표본 분포에 대한 수리통계학 강의자료 입니다.
- 가설 검정 이론 1 : 가설 검정 이론에 대한 수리통계학 강의자료1 입니다.
- 가설 검정 이론 2 : 가설 검정 이론에 대한 수리통계학 강의자료2 입니다.
- 분산분석 : 분산 분석(ANOVA) 대한 수리통계학 강의 자료입니다.
- 상관분석 : 상관 분석에 대한 수리통계학 강의 자료입니다.
- Python code
  - Hypothesis python code

Lecture11 (03/27) : 머신러닝 기초 (Machine Learning Basic)

Machine learning의 basic concept에 대한 강의 자료입니다.
Reading material
- Machine Learning (Tom Mitchell)
- The Discipline of Machine Learning

Lecture12 (03/30) : 베이지안 결정 이론(Bayesian Decision Theory)

Bayesian Decision Theory에 대한 강의 자료입니다.

Lecture12 sub notes: MLE/MAP : MLE/MAP에 대한 강의 자료입니다.
Naive Bayesian From The Scratch : Naive bayesian을 이용한 sentiment analysis 1 입니다.
Naive Bayesian Sklearn Code : Naive bayesian을 이용한 sentiment analysis 2 입니다.
GNB Sklearn Code : GNB를 이용하여 iris 데이터를 분류하는 예제입니다.
Reading material
- Stanford CS229 : Generative Learning algorithms
- Pattern Recoginition and Machine Learning(Bishop): 1.5 Decision Theory (번역본있음)
- Pattern Classification(Duda) : 2. Bayesian Decision Theory (번역본있음)

Lecture13 (04/03, 04/06) : 선형 회귀 분석(Linear Regression)

Linear Regression에 대한 강의 자료입니다.

Linear Regression Statistical models
Linear Regression ML models
Reading material
- A Few Useful Things to Know about Machine Learning : 머신러닝에 굉장히 유용한 논문입니다.
- Stanford CS229 : Linear Regression

Lecture14 (04/10) : 분류(Classification) 1

Binary Classification에 대한 강의 자료입니다. (핸즈온 머신러닝)

Lecture14 (04/10) : 분류(Classification) 2

Muticlass Classification에 대한 강의 자료입니다. (핸즈온 머신러닝)

Lecture15 (04/13, 04/24) : 로지스틱 회귀(Logistic Regression)

Logistic Regression에 대한 강의 자료입니다.

Logistic Regression Statsmodels Code
Logistic Regression Sklearn Code
Reading material
- Stanford CS229 : Logistic Regression

Lecture16 (04/27) : 정보이론 및 결정트리(Information theory / Decision Tree)

Decision tree에 대한 강의 자료입니다.

Lecture17 (05/04) : Ensemble 1 (Bagging, Random forest)

Ensemble method 중에서 bagging과 Random forest에 대한 강의 자료입니다.

Lecture17 (05/08) : Ensemble 2 (Boosting)

Ensemble method 중에서 boosting과 stacking에 대한 강의 자료입니다.

Lecture18 (05/15) : 서포트 벡터 머신(Support Vector Machine)

SVM에 대한 강의 자료입니다.

SVM Python Code : SVM Python Code 강의 자료입니다.
Reading material
- Stanford CS229 : SVM : SVM 참고자료 입니다.
- Stanford lecture note for SMO : SMO 참고자료 입니다.
- Convex Optimization Lecture Note : Convex optimization을 정리한 내용 입니다.
- Convex Optimization Book

Lecture19 (05/22) : 주성분 분석(Principal Components Analysis)

PCA에 대한 강의 자료입니다.

PCA Python Code 1 : PCA from the scratch 입니다. PCA를 직접 손으로 구현해 봅니다.
PCA Python Code 2 : Sklearn을 활용하여, PCA를 시각화하고, 적절한 주성분의 갯수를 찾는 방법에 관한 자료입니다.
PCA Python Code 3 : Kaggle의 benz문제를 PCA를 활용하여 해결하는 자료입니다.
Reading materials
- Stanford CS229 : PCA

Lecture20 (06/01) : K-means clustering

K-means algorithms에 대한 강의 자료입니다.

K-means Python Code 1 : K-means clustering 및 적절한 클러스터의 갯수를 찾는 방법에 관한 자료입니다.
K-means Python Code 2 : K-means python code 입니다.
Reading materials
- Stanford CS229 : K-means
- Stanford CS229 : Gaussian Mixture Model

Lecture21 (06/05) : Clustering

Segmentations 1 : UCI e-commerce data를 활용하여 user segmentation을 실습하는 간단한 자료입니다.
Segmentations 2 : UCI e-commerce data를 활용하여 user segmentation을 실습하는 분석난이도가 있는 자료입니다.

Lecture22 (06/08) : 심층 신경망 기본 + Keras(Deep Neural Network)

Perceptron 및 Deep Neural Network에 대한 강의자료입니다.

DNN Python Code 1 : Keras를 이용하여 기본적인 분류 및 회귀문제를 DNN을 이용하여 해결하는 예제입니다.
DNN and Sampling Python Code 1 : Kaggle의 card fraud detection 문제를 해결하며, imbalanced 문제를 다루는 예제입니다.
Reading material

Lecture23 (06/12) : Feature selection

Feature selection에 대한 강의 자료입니다.

Feature selection Python Code 1

Lecture24 (06/15) : Time-series-analysis

Lecture25 : Association-rule-mining

Association Rule에 대한 강의 자료입니다.

Lecture26 : Topic modeling

토픽 모델링에 대한 강의 자료입니다.

Name		Name	Last commit message	Last commit date
Latest commit History 258 Commits
images		images
inputs		inputs
03_classification.ipynb		03_classification.ipynb
Lecture01_Machine_Learning_Simple_Tutorial.ipynb		Lecture01_Machine_Learning_Simple_Tutorial.ipynb
Lecture02_Linear_Regression.ipynb		Lecture02_Linear_Regression.ipynb
Lecture02_Probabilities.pdf		Lecture02_Probabilities.pdf
Lecture03_Probability_Distribution.ipynb		Lecture03_Probability_Distribution.ipynb
Lecture03_Probability_Distribution_01.pdf		Lecture03_Probability_Distribution_01.pdf
Lecture03_Probability_Distribution_02.pdf		Lecture03_Probability_Distribution_02.pdf
Lecture04_Linear_Algebra_Basic_Matrix.pdf		Lecture04_Linear_Algebra_Basic_Matrix.pdf
Lecture04_Linear_Algebra_Basic_Vector.pdf		Lecture04_Linear_Algebra_Basic_Vector.pdf
Lecture04_Sub_Vectors-and-Matrices.ipynb		Lecture04_Sub_Vectors-and-Matrices.ipynb
Lecture05_Numpy.ipynb		Lecture05_Numpy.ipynb
Lecture05_Sub_KNN_Using_Numpy.ipynb		Lecture05_Sub_KNN_Using_Numpy.ipynb
Lecture05_Sub_Numpy_Problem01.ipynb		Lecture05_Sub_Numpy_Problem01.ipynb
Lecture06_Hypothesis_Test.ipynb		Lecture06_Hypothesis_Test.ipynb
Lecture06_Spectral_Theorem_Eigenvalue.pdf		Lecture06_Spectral_Theorem_Eigenvalue.pdf
Lecture06_Spectral_Theorem_Transformation.pdf		Lecture06_Spectral_Theorem_Transformation.pdf
Lecture06_Sub_LSA.ipynb		Lecture06_Sub_LSA.ipynb
Lecture06_Sub_LSA_2.ipynb		Lecture06_Sub_LSA_2.ipynb
Lecture07_Pandas_1.ipynb		Lecture07_Pandas_1.ipynb
Lecture07_Pandas_2.ipynb		Lecture07_Pandas_2.ipynb
Lecture07_Sub_Pandas_Analysis_Examples.ipynb		Lecture07_Sub_Pandas_Analysis_Examples.ipynb
Lecture07_Sub_Pandas_Problem01.ipynb		Lecture07_Sub_Pandas_Problem01.ipynb
Lecture08_Matplotlib_1.ipynb		Lecture08_Matplotlib_1.ipynb
Lecture08_Matplotlib_2.ipynb		Lecture08_Matplotlib_2.ipynb
Lecture09_Gradient.pdf		Lecture09_Gradient.pdf
Lecture09_Sub_Gradient_01.ipynb		Lecture09_Sub_Gradient_01.ipynb
Lecture09_Sub_Gradient_02.ipynb		Lecture09_Sub_Gradient_02.ipynb
Lecture10_Anova.pdf		Lecture10_Anova.pdf
Lecture10_Correlation.pdf		Lecture10_Correlation.pdf
Lecture10_EDA.ipynb		Lecture10_EDA.ipynb
Lecture10_Estimation_Theory.pdf		Lecture10_Estimation_Theory.pdf
Lecture10_Hypothesis_Testing_01.pdf		Lecture10_Hypothesis_Testing_01.pdf
Lecture10_Hypothesis_Testing_02.pdf		Lecture10_Hypothesis_Testing_02.pdf
Lecture10_Sample_Distribution.pdf		Lecture10_Sample_Distribution.pdf
Lecture10_Statistics_In_Data_Science_1.ipynb		Lecture10_Statistics_In_Data_Science_1.ipynb
Lecture10_Statistics_In_Data_Science_2.ipynb		Lecture10_Statistics_In_Data_Science_2.ipynb
Lecture11_Basic_Concept_of_Machine_Learning.pdf		Lecture11_Basic_Concept_of_Machine_Learning.pdf
Lecture11_Gaussian_Naive_Bayes.ipynb		Lecture11_Gaussian_Naive_Bayes.ipynb
Lecture11_Sentiment_Classifier_Using_Naive_Bayes_From_The_Scratch.ipynb		Lecture11_Sentiment_Classifier_Using_Naive_Bayes_From_The_Scratch.ipynb
Lecture11_Sentiment_Classifier_Using_Naive_Bayes_With_SKlearn.ipynb		Lecture11_Sentiment_Classifier_Using_Naive_Bayes_With_SKlearn.ipynb
Lecture12_Bayesian_Decision_Thoery.pdf		Lecture12_Bayesian_Decision_Thoery.pdf
Lecture12_MLE_MAP.pdf		Lecture12_MLE_MAP.pdf
Lecture13_Linear_Regression.pdf		Lecture13_Linear_Regression.pdf
Lecture13_Linear_Regression_ML_Model.ipynb		Lecture13_Linear_Regression_ML_Model.ipynb
Lecture13_Linear_Regression_Stat_Model.ipynb		Lecture13_Linear_Regression_Stat_Model.ipynb
Lecture14_Binary_Classification_MNIST.ipynb		Lecture14_Binary_Classification_MNIST.ipynb
Lecture14_Multiclass_Classification_MNIST.ipynb		Lecture14_Multiclass_Classification_MNIST.ipynb
Lecture15_Logistic_Regression.pdf		Lecture15_Logistic_Regression.pdf
Lecture15_Logistic_Regression_ML.ipynb		Lecture15_Logistic_Regression_ML.ipynb
Lecture15_Logistic_Regression_Stat.ipynb		Lecture15_Logistic_Regression_Stat.ipynb
Lecture16_Decision_Tree_Information_Theory.pdf		Lecture16_Decision_Tree_Information_Theory.pdf
Lecture16_Decision_Tree_Python_1.ipynb		Lecture16_Decision_Tree_Python_1.ipynb
Lecture16_Decision_Tree_Python_2.ipynb		Lecture16_Decision_Tree_Python_2.ipynb
Lecture17_Creating_Random_Forest.ipynb		Lecture17_Creating_Random_Forest.ipynb
Lecture17_Ensemble_1.ipynb		Lecture17_Ensemble_1.ipynb
Lecture17_Ensemble_2.ipynb		Lecture17_Ensemble_2.ipynb
Lecture17_Ensemble_Models_1.pdf		Lecture17_Ensemble_Models_1.pdf
Lecture17_Ensemble_Models_2.pdf		Lecture17_Ensemble_Models_2.pdf
Lecture17_Stacking.ipynb		Lecture17_Stacking.ipynb
Lecture18_Convex_optimization.pdf		Lecture18_Convex_optimization.pdf
Lecture18_SVM.ipynb		Lecture18_SVM.ipynb
Lecture18_SVM.pdf		Lecture18_SVM.pdf
Lecture19_Dimesional_Reduction_With_PCA.ipynb		Lecture19_Dimesional_Reduction_With_PCA.ipynb
Lecture19_PCA.ipynb		Lecture19_PCA.ipynb
Lecture19_PCA.pdf		Lecture19_PCA.pdf
Lecture19_PCA_By_Hands.ipynb		Lecture19_PCA_By_Hands.ipynb
Lecture20_Customer_Segmentation_Easy_version.ipynb		Lecture20_Customer_Segmentation_Easy_version.ipynb
Lecture20_Customer_Segmentation_Full_Version.ipynb		Lecture20_Customer_Segmentation_Full_Version.ipynb
Lecture20_K_Means_Clustering.ipynb		Lecture20_K_Means_Clustering.ipynb
Lecture20_K_means_clustering.pdf		Lecture20_K_means_clustering.pdf
Lecture21_Clustering.ipynb		Lecture21_Clustering.ipynb
Lecture22_CardFraudDetection.ipynb		Lecture22_CardFraudDetection.ipynb
Lecture22_DNN.ipynb		Lecture22_DNN.ipynb
Lecture22_DNN.pdf		Lecture22_DNN.pdf
Lecture23_Feature_Selection.pdf		Lecture23_Feature_Selection.pdf
Lecture24_Time_Series_01.ipynb		Lecture24_Time_Series_01.ipynb
Lecture24_Time_Series_02.ipynb		Lecture24_Time_Series_02.ipynb
Lecture25_Association_Rule_Mining.pdf		Lecture25_Association_Rule_Mining.pdf
Lecture26_Topic_models.pdf		Lecture26_Topic_models.pdf
README.md		README.md
utilizing-arima-to-forecast-uber-s-market-demand.ipynb		utilizing-arima-to-forecast-uber-s-market-demand.ipynb

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machine-learning-lecture-notes

Lecture21 (06/05) : Clustering

Lecture24 (06/15) : Time-series-analysis

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