Hafta 114–20 Eyl
Statistical learning ve R'a giriş
Introduction to statistical learning and R, overview of regression and classification problems
statistical learningRregressionclassification
Hafta 221–27 Eyl
Linear regression uygulamaları
Linear regression (Illustrations: effects of budgets allocated for TV, newspaper, radio advertisement on annual sales, prediction of credit card balance from income, limit, rating, age, number of cards, and education level)
linear regressionadvertising budgetcredit card balanceprediction
Hafta 328 Eyl – 4 Eki
Linear regression ve k-nearest neighbour regression
Linear regression continued and k-nearest neighbour regression (Illustration: conjoint analysis from marketing science; how can you design a new product with a higher market penetration?)
linear regressionk-nearest neighbour regressionconjoint analysismarket penetration
Hafta 45–11 Eki
Logistic regression ile loan default tahmini
Logistic regression (Illustration: loan default probability estimation from credit card balance, income, occupation)
logistic regressionloan default probabilitycredit card balanceincome
Hafta 512–18 Eki
Multinomial ve Poisson regression uygulamaları
Multinomial and Poisson regressions (Illustration: would it have been possible to predict the Challenger diasaster? https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster)
multinomial regressionPoisson regressionChallenger disaster
Hafta 619–25 Eki
Linear discriminant analysis uygulamaları
Linear discriminant analysis (Illustrations: revisit credit card default probability estimation and Challenger disaster)
linear discriminant analysiscredit card defaultChallenger disaster
Hafta 726 Eki – 1 Kas
Cross-validation ve linear model selection
Cross-validation, linear model selection, subset selection (Illustrations: what are the variables among income, limit, rating, age, number of cards, and education level that explain the credit card balance or default probability best? Is logistic regression or linear discriminant model best for predicting the loan default probability?)
cross-validationsubset selectionlogistic regressionlinear discriminant
Hafta 82–8 Kas
Shrinkage yöntemleri: ridge regression ve lasso
Shrinkage methods, ridge regression and lasso (What if the number of predictors is large--comparable to number of examples? Illustration: prediction of salaries of baseball players from various measures of their performances in the past games)
shrinkageridge regressionlassopredictor sayısı
Hafta 99–15 Kas
Polynomial Regression ve Spline Yöntemleri
Polynomial regression, regression splines, smoothing splines (Illustration: modeling the wage as a function of age, the amount pollutants in a residential area as a function of its distance from employment centers)
polynomial regressionregression splinessmoothing splineswage-age modeli
Hafta 1016–22 Kas
Local regression ve generalized additive models
Local regression, generalized additive models for quantitative and categorical variables (Illustrations: revisit wage and pollutant examples)
local regressiongeneralized additive modelsquantitative ve categorical değişkenlerwage ve pollutant örnekleri
Hafta 1123–29 Kas
Regression tree uygulamaları: maaş ve satış tahmini
Regression trees (Illustrations: predict the baseball player salaries, car-seat sales)
regression treebaseball player salariescar-seat sales
Hafta 1230 Kas – 6 Ara
Classification tree uygulamaları
Classification trees (Illustrations: email spam filtering--when is an email message spam? Predict crime rate in a residential area)
classification treesemail spam filteringcrime rate prediction
Hafta 137–13 Ara
Bagging, random forest ve boosting
Bagging, random forests, boosting (Illustrations: revisit baseball player salary email spam, crime-rate examples)
baggingrandom forestsboostingbaseball salary / email spam / crime-rate örnekleri
Hafta 1414–20 Ara
PCA ve clustering yöntemleri, uygulamaları
Principal component analysis, k-means and hierarchical clustering (Illustrations: handwritten digit recognition, clustering cancer cell according to micro-array data, market-basket data)
principal component analysisk-meanshierarchical clusteringmarket-basket data