{"id":21578818,"url":"https://github.com/jeonghunyoon/machine-learning-lecture-notes","last_synced_at":"2025-04-10T17:41:53.325Z","repository":{"id":185670462,"uuid":"171694481","full_name":"jeonghunyoon/machine-learning-lecture-notes","owner":"jeonghunyoon","description":"Lecture notes and codes for machine learning","archived":false,"fork":false,"pushed_at":"2019-08-12T08:30:06.000Z","size":110752,"stargazers_count":12,"open_issues_count":0,"forks_count":9,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-24T15:21:48.212Z","etag":null,"topics":["data-science","decision-tree","deep-learning","lecture-notes","linear-algebra","linear-regression","lsa","machine-learning","naive-bayes-classifier","statistics"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jeonghunyoon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2019-02-20T15:06:13.000Z","updated_at":"2022-04-12T14:55:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"7c7b97e4-28ff-4dd2-b8cc-8b13d15f1374","html_url":"https://github.com/jeonghunyoon/machine-learning-lecture-notes","commit_stats":null,"previous_names":["jeonghunyoon/machine-learning-lecture-notes"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeonghunyoon%2Fmachine-learning-lecture-notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeonghunyoon%2Fmachine-learning-lecture-notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeonghunyoon%2Fmachine-learning-lecture-notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeonghunyoon%2Fmachine-learning-lecture-notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jeonghunyoon","download_url":"https://codeload.github.com/jeonghunyoon/machine-learning-lecture-notes/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248262192,"owners_count":21074261,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","decision-tree","deep-learning","lecture-notes","linear-algebra","linear-regression","lsa","machine-learning","naive-bayes-classifier","statistics"],"created_at":"2024-11-24T13:11:39.399Z","updated_at":"2025-04-10T17:41:53.307Z","avatar_url":"https://github.com/jeonghunyoon.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# machine-learning-lecture-notes\n\n이 강의자료는 데이터 사이언티스트 스쿨에서 사용하는 강의 교재입니다. 머신러닝 및 데이터과학 그리고 딥러닝을 다루고 있습니다.\n\n### [Lecture01 (02/20) : 머신러닝 입문을 위한 간단한 튜토리얼](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture01_Machine_Learning_Simple_Tutorial.ipynb)\n머신러닝 입문을 위한 간단한 튜토리얼입니다. 캘리포니아 지역의 (블록내의) 집 값을 예측하는 튜토리얼이며 머신러닝 프로젝트의 시작부터 끝까지를 간단하게 보여주고 있습니다. (핸즈 온 머신러닝)\n\n### [Lecture02 (02/23) : 확률론](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture02_Probabilities.pdf?flush_cache=true)\n기초 확률론에 대한 수리통계학 강의자료입니다. \n\n### [Lecture03 (02/23) : 확률분포 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture03_Probability_Distribution_01.pdf?flush_cache=true)\n확률 분포에 대한 수리통계학 강의자료1 입니다. \n\n### [Lecture03 (02/23) : 확률분포 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture03_Probability_Distribution_02.pdfd )\n확률 분포에 대한 수리통계학 강의자료2 입니다.\n - [Probability Distribution python code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture03_Probability_Distribution.ipynb?flush_cache=true)\n\n### [Lecture04 (02/27) : 선형대수 기초 01 (벡터)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture04_Linear_Algebra_Basic_Vector.pdf?flush_cache=true)\n선형 대수에서 벡터 내용입니다.\n\n### [Lecture04 (02/27) : 선형대수 기초 02 (행렬)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture04_Linear_Algebra_Basic_Matrix.pdf?flush_cache=true)\n선형대수 행렬 내용입니다.\n - [Vector And Matrix Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture04_Sub_Vectors-and-Matrices.ipynb?flush_cache=true)\n\n### [Lecture05 (03/02) : Numpy](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture05_Numpy.ipynb?flush_cache=true)\nData 분석에 필요한 Numpy 기본편 입니다.\n - [KNN Numpy Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture05_Sub_KNN_Using_Numpy.ipynb?flush_cache=true)\n - [Numpy Problem Set](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture05_Sub_Numpy_Problem01.ipynb?flush_cache=true)\n\n### [Lecture06 (03/06) : 선형대수 기초 03 (변환)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture06_Spectral_Theorem_Transformation.pdf?flush_cache=true)\n선형대수 변환 내용입니다.\n\n### [Lecture06 (03/06) : 선형대수 기초 04 (고유값/고유벡터)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture06_Spectral_Theorem_Eigenvalue.pdf?flush_cache=true)\n선형대수 고유값/고유벡터 내용입니다.\n - [LSA From The Scratch Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture06_Sub_LSA.ipynb?flush_cache=true)\n  - [LSA From The Scratch Python Code 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture06_Sub_LSA_2.ipynb?flush_cache=true)\n\n### [Lecture07 (03/09) : Pandas 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture07_Pandas_1.ipynb?flush_cache=true)\n데이터 분석에 필요한 Pandas 기본편 1 입니다. \n\n### [Lecture08 (03/13) : Pandas 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture07_Pandas_2.ipynb?flush_cache=true)\n데이터 분석에 필요한 Pandas 기본편 2 입니다.\n - [Data Analysis Example](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture07_Sub_Pandas_Analysis_Examples.ipynb?flush_cache=true)\n - [Pandas Problem Set](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture07_Sub_Pandas_Problem01.ipynb?flush_cache=true)\n - [Matplotlib_01](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture08_Matplotlib_1.ipynb?flush_cache=true) : 데이터 시각화 툴인 Matplotlib 1 입니다.\n - [Matplotlib_02](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture08_Matplotlib_2.ipynb?flush_cache=true) : 데이터 시각화 툴인 Matplotlib 1 입니다.\n\n### [Lecture09 (03/16) : 기본 미적분학](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture09_Gradient.pdf)\nGradient를 계산할 때 사용되는 기본 미적분학 강의자료입니다.\n - [Gradient Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture09_Sub_Gradient_01.ipynb)\n - [Gradient Python Code 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture09_Sub_Gradient_02.ipynb)\n\n### [Lecture10 (03/20) : EDA](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_EDA.ipynb)\n - EDA에 대한 강의 자료입니다.\n\n### [Lecture10 (03/20) : Statistics in Data Science 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Statistics_In_Data_Science_1.ipynb)\n - Data Science 에서 필요한 통계 내용에 대한 강의 자료1 입니다.\n\n### [Lecture10 (03/23) : Statistics in Data Science 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Statistics_In_Data_Science_2.ipynb)\n - Reading material\n      - [추정 이론](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Estimation_Theory.pdf?flush_cache=true) : \n추정 이론에 대한 수리통계학 강의자료 입니다.\n     - [표본 분포 이론](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Sample_Distribution.pdf?flush_cache=true) : 표본 분포에 대한 수리통계학 강의자료 입니다.\n     - [가설 검정 이론 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Hypothesis_Testing_01.pdf?flush_cache=true) : 가설 검정 이론에 대한 수리통계학 강의자료1 입니다.\n     - [가설 검정 이론 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Hypothesis_Testing_02.pdf?flush_cache=true) : 가설 검정 이론에 대한 수리통계학 강의자료2 입니다.\n     - [분산분석](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Anova.pdf?flush_cache=true) : 분산 분석(ANOVA) 대한 수리통계학 강의 자료입니다.\n     - [상관분석](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture10_Correlation.pdf?flush_cache=true) : 상관 분석에 대한 수리통계학 강의 자료입니다.\n     - Python code\n         - [Hypothesis python code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture06_Hypothesis_Test.ipynb?flush_cache=true)\n         \n### [Lecture11 (03/27) : 머신러닝 기초 (Machine Learning Basic)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture11_Basic_Concept_of_Machine_Learning.pdf)\n - Machine learning의 basic concept에 대한 강의 자료입니다.\n - Reading material\n     - Machine Learning (Tom Mitchell)\n     - [The Discipline of Machine Learning](http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf)\n\n### [Lecture12 (03/30) : 베이지안 결정 이론(Bayesian Decision Theory)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture12_Bayesian_Decision_Thoery.pdf)\nBayesian Decision Theory에 대한 강의 자료입니다.\n - [Lecture12 sub notes: MLE/MAP](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture12_MLE_MAP.pdf) : MLE/MAP에 대한 강의 자료입니다.\n - [Naive Bayesian From The Scratch](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture11_Sentiment_Classifier_Using_Naive_Bayes_From_The_Scratch.ipynb) : Naive bayesian을 이용한 sentiment analysis 1 입니다.\n - [Naive Bayesian Sklearn Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture11_Sentiment_Classifier_Using_Naive_Bayes_With_SKlearn.ipynb) : Naive bayesian을 이용한 sentiment analysis 2 입니다.\n - [GNB Sklearn Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture11_Gaussian_Naive_Bayes.ipynb) : GNB를 이용하여 iris 데이터를 분류하는 예제입니다.\n - Reading material\n      - [Stanford CS229 : Generative Learning algorithms](http://cs229.stanford.edu/notes/cs229-notes2.pdf)\n      - Pattern Recoginition and Machine Learning(Bishop): 1.5 Decision Theory (번역본있음)\n      - Pattern Classification(Duda) : 2. Bayesian Decision Theory (번역본있음)\n\n### [Lecture13 (04/03, 04/06) : 선형 회귀 분석(Linear Regression)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture13_Linear_Regression.pdf?flush_cache=true)\nLinear Regression에 대한 강의 자료입니다.\n - [Linear Regression Statistical models](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture13_Linear_Regression_Stat_Model.ipynb)\n - [Linear Regression ML models](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture13_Linear_Regression_ML_Model.ipynb)\n - Reading material\n     - [A Few Useful Things to Know about Machine Learning](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) : 머신러닝에 굉장히 유용한 논문입니다.\n     - [Stanford CS229 : Linear Regression](http://cs229.stanford.edu/notes/cs229-notes1.pdf)\n\n### [Lecture14 (04/10) : 분류(Classification) 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture14_Binary_Classification_MNIST.ipynb)\nBinary Classification에 대한 강의 자료입니다. (핸즈온 머신러닝)\n\n### [Lecture14 (04/10) : 분류(Classification) 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture14_Multiclass_Classification_MNIST.ipynb)\nMuticlass Classification에 대한 강의 자료입니다. (핸즈온 머신러닝)\n\n### [Lecture15 (04/13, 04/24) : 로지스틱 회귀(Logistic Regression)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture15_Logistic_Regression.pdf)\nLogistic Regression에 대한 강의 자료입니다.\n - [Logistic Regression Statsmodels Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture15_Logistic_Regression_Stat.ipynb)\n - [Logistic Regression Sklearn Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture15_Logistic_Regression_ML.ipynb)\n - Reading material\n     - [Stanford CS229 : Logistic Regression](http://cs229.stanford.edu/notes/cs229-notes1.pdf)\n\n### [Lecture16 (04/27) : 정보이론 및 결정트리(Information theory / Decision Tree)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture16_Decision_Tree_Information_Theory.pdf)\nDecision tree에 대한 강의 자료입니다.\n - [Decision Tree Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture16_Decision_Tree_Python_1.ipynb)\n - [Decision Tree Python Code 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture16_Decision_Tree_Python_2.ipynb)\n \n\n### [Lecture17 (05/04) : Ensemble 1 (Bagging, Random forest)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Ensemble_Models_1.pdf)\nEnsemble method 중에서 bagging과 Random forest에 대한 강의 자료입니다.\n- [Ensemble - Bagging, Random Forest Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Ensemble_1.ipynb)\n- [Creating simple Random Forest](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Creating_Random_Forest.ipynb)\n\n### [Lecture17 (05/08) : Ensemble 2 (Boosting)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Ensemble_Models_2.pdf)\nEnsemble method 중에서 boosting과 stacking에 대한 강의 자료입니다.\n- [Ensemble - Boosting Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Ensemble_2.ipynb)\n- [Ensemble - Stacking Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture17_Stacking.ipynb)\n- Reading material\n    - [Stanford lecture note](https://web.stanford.edu/~hastie/TALKS/boost.pdf)\n    - [Northeastern Univ. lecture note](http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf) \n    - [Stanford CS229 : Ensemble](http://cs229.stanford.edu/notes/cs229-notes-ensemble.pdf)\n\n### [Lecture18 (05/15) : 서포트 벡터 머신(Support Vector Machine)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture18_SVM.pdf)\nSVM에 대한 강의 자료입니다.\n- [SVM Python Code](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture18_SVM.ipynb) : SVM Python Code 강의 자료입니다.\n- Reading material\n    - [Stanford CS229 : SVM](http://cs229.stanford.edu/notes/cs229-notes3.pdf) : SVM 참고자료 입니다.\n    - [Stanford lecture note for SMO](http://cs229.stanford.edu/materials/smo.pdf) : SMO 참고자료 입니다.\n    - [Convex Optimization Lecture Note](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture18_Convex_optimization.pdf) : Convex optimization을 정리한 내용 입니다.\n    - [Convex Optimization Book](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf)\n\n### [Lecture19 (05/22) : 주성분 분석(Principal Components Analysis)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture19_PCA.pdf)\nPCA에 대한 강의 자료입니다.\n- [PCA Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture19_PCA_By_Hands.ipynb) : PCA from the scratch 입니다. PCA를 직접 손으로 구현해 봅니다.\n- [PCA Python Code 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture19_PCA.ipynb) : Sklearn을 활용하여, PCA를 시각화하고, 적절한 주성분의 갯수를 찾는 방법에 관한 자료입니다.\n- [PCA Python Code 3](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture19_Dimesional_Reduction_With_PCA.ipynb) : Kaggle의 benz문제를 PCA를 활용하여 해결하는 자료입니다.\n- Reading materials\n    - [Stanford CS229 : PCA](http://cs229.stanford.edu/notes/cs229-notes10.pdf)\n    \n### [Lecture20 (06/01) : K-means clustering](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture20_K_means_clustering.pdf)\nK-means algorithms에 대한 강의 자료입니다.\n- [K-means Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture20_K_Means_Clustering.ipynb) : K-means clustering 및 적절한 클러스터의 갯수를 찾는 방법에 관한 자료입니다.\n- [K-means Python Code 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture21_Clustering.ipynb) : K-means python code 입니다.\n- Reading materials\n    - [Stanford CS229 : K-means](http://cs229.stanford.edu/notes/cs229-notes7a.pdf)\n    - [Stanford CS229 : Gaussian Mixture Model](http://cs229.stanford.edu/notes/cs229-notes7b.pdf)\n\n### Lecture21 (06/05) : Clustering\n- [Segmentations 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture20_Customer_Segmentation_Easy_version.ipynb) : UCI e-commerce data를 활용하여 user segmentation을 실습하는 간단한 자료입니다.\n- [Segmentations 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture20_Customer_Segmentation_Full_Version.ipynb) : UCI e-commerce data를 활용하여 user segmentation을 실습하는 분석난이도가 있는 자료입니다.\n\n### [Lecture22 (06/08) : 심층 신경망 기본 + Keras(Deep Neural Network)](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture22_DNN.pdf)\nPerceptron 및 Deep Neural Network에 대한 강의자료입니다.\n- [DNN Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture22_DNN.ipynb) : Keras를 이용하여 기본적인 분류 및 회귀문제를 DNN을 이용하여 해결하는 예제입니다.\n- [DNN and Sampling Python Code 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture22_CardFraudDetection.ipynb) : Kaggle의 card fraud detection 문제를 해결하며, imbalanced 문제를 다루는 예제입니다.\n- Reading material\n    - [Stanford CS229 : Perceptron](http://cs229.stanford.edu/notes/cs229-notes-deep_learning.pdf)\n    - [Stanford CS229 : Back Propergation](http://cs229.stanford.edu/notes/cs229-notes-backprop.pdf)\n    - [Google Dev Demo](https://google-developers.appspot.com/machine-learning/crash-course/backprop-scroll/)\n\n### [Lecture23 (06/12) : Feature selection](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture23_Feature_Selection.pdf)\nFeature selection에 대한 강의 자료입니다.\n- [Feature selection Python Code 1](https://scikit-learn.org/stable/modules/feature_selection.html)\n\n### Lecture24 (06/15) : Time-series-analysis\n- [ARIMA 1](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture24_Time_Series_01.ipynb)\n- [ARIMA 2](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture24_Time_Series_02.ipynb)\n\n### [Lecture25 : Association-rule-mining](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture25_Association_Rule_Mining.pdf)\nAssociation Rule에 대한 강의 자료입니다.\n\n### [Lecture26 : Topic modeling](https://nbviewer.jupyter.org/github/jeonghunyoon/machine-learning-lecture-notes/blob/master/Lecture26_Topic_models.pdf)\n토픽 모델링에 대한 강의 자료입니다.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeonghunyoon%2Fmachine-learning-lecture-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjeonghunyoon%2Fmachine-learning-lecture-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeonghunyoon%2Fmachine-learning-lecture-notes/lists"}