Zeitplan

Die folgende Tabelle enthält die Videos als mp4-Dateien zum Download und damit zum Offline-Betrachten.

Zusätzliche relevante Links und Dateien:

Gesamtfoliensatz zu allen Videos (Quelle: Hastie, Tibshirani, Stanford Online, edX)
Zip-Datei aller (korrigierten) srt-Dateien mit den englischen Untertiteln zu den Videos; abspielbar z.B. mit dem VLC Player
Übersichtliche Darstellung der gesprochenen Texte zusammen mit den Folieninhalten („Transcript“)
YouTube-Playlist; enthält zusätzliche Videos zur 2. Auflage des Begleitbuchs, die für unseren Kurs nicht relevant sind
Der zugehörige Moodle-Kurs enthält Fragen zum Selbsttest, die aus den jeweiligen Videos zu beantworten sind.

Datum	Themen	Buchkapitel	Vorab anzusehende Videos zum Download (YTa, YTb: YouTube-Links zum Online-Betrachten in höherer Auflösung) (Dauer) Quelle: Hastie, Tibshirani, Stanford Online, edX	Beispiele für Präsentationen der Kursteilnehmer
7.10.	Organisatorisches, Überblick, Kurzeinführung in R, RStudio und R Markdown	Preface Contents 1 Introduction	Promo Video (2:15) Ch1_1 Opening Remarks and Examples (YTa, YTb) (18:18) Ch1_2 Supervised and Unsupervised Learning (YTa, YTb) (12:12)	–
14.10.	Supervised Learning: Regression und Klassifikation, K-nearest neighbors (KNN); Overfitting (Überanpassung)	2 Statistical Learning	Ch2_1 Statistical Learning and Regression (YTa, YTb) (11:41) Ch2_2 Curse of Dimensionality and Parametric Models (YTa, YTb) (11:40) Ch2_3 Assessing Model Accuracy and Bias-Variance Trade-off (YTa, YTb) (10:04) Ch2_4 Classification Problems and K-Nearest Neighbors (YTa, YTb) (15:37) Ch2_5 Lab: Introduction to R (YTa, YTb) (14:12) optional: Interview with John Chambers (YT) (10:20)	–
21.10.	Lineare und polynomiale Regression	3 Linear Regression	Ch3_1 Simple Linear Regression and Confidence Intervals (YTa, YTb) (13:01) Ch3_2 Hypothesis Testing (YTa, YTb) (8:24) Ch3_3 Multiple Linear Regression and Interpreting Regression Coefficients (YTa, YTb) (15:38) Ch3_4 Model Selection and Qualitative Predictors (YTa, YTb) (14:51) Ch3_5 Interactions and Nonlinearity (YTa, YTb) (14:16) Ch3_6 Lab: Linear Regression (YTa, YTb) (22:10)	Ex. 8 and 9 (p. 123/124): Simple and multiple linear regression on the Auto data set Ex. 10 (p. 124/125): Linear regression on the Carseats data set Ex. 15 (p. 128): Multiple linear regression on the Boston data set
28.10.	Logistische Regression und lineare Diskriminanzanalyse (LDA)	4 Classification	Ch4_1 Introduction to Classification (YTa, YTb) (10:25) Ch4_2 Logistic Regression and Maximum Likelihood (YTa) (9:07) Ch4_3 Multivariate Logistic Regression and Confounding (YTa) (9:53) Ch4_4 Case-Control Sampling and Multiclass Logistic Regression (YTa, YTb) (7:28) Ch4_5 Linear Discriminant Analysis and Bayes Theorem (YTa, YTb) (7:12) Ch4_6 Univariate Linear Discriminant Analysis (YTa) (7:37) Ch4_7 Multivariate Linear Discriminant Analysis and ROC Curves (YTa, YTb) (17:42) Ch4_8 Quadratic Discriminant Analysis and Naive Bayes (YTa) (10:07) Ch4_9 Lab1: Logistic Regression (YTa, YTb) (10:14) Ch4_10 Lab2: Linear Discriminant Analysis (YTa, YTb) (8:22) Ch4_11 Lab3: K-Nearest Neighbors (YTa, YTb) (5:01)	Ex. 13 (p. 193): Classification of the Weekly data set Ex. 14 (p. 194): Classification of the Auto data set Ex. 16 (p. 195): Classification of the Boston data set
4.11.	Cross-Validation und Bootstrap	5 Resampling Methods	Ch5_1 Estimating Prediction Error and Validation Set Approach (YTa, YTb) (14:01) Ch5_2 K-fold Cross-Validation (YTa, YTb) (13:33) Ch5_3 Cross-Validation: The Right and Wrong Ways (YTa, YTb) (10:07) Ch5_4 The Bootstrap (YTa, YTb) (11:29) Ch5_5 More on the Bootstrap (YTa, YTb) (14:35) Ch5_6 Lab1: Cross-Validation (YTa, YTb) (11:21) Ch5_7 Lab2: The Bootstrap (YTa, YTb) (7:40) optional: Interview with Bradley Efron (YT) (12:08)	Ex. 5 and 6 (p. 220/221): Validation set approach and using the bootstrap on the Default data set Ex. 7 (p. 222) and 9 (p. 223): Implementing leave-one-out cross-validation (LOOCV) on the Weekly data set and using the bootstrap on the Boston data set
11.11.	Modellauswahl und Regularisierung (Ridge Regression und Lasso)	6 Linear Model Selection and Regularization	Ch6_1 Linear Model Selection and Best Subset Selection (YTa, YTb) (13:44) Ch6_2 Forward Stepwise Selection (YTa, YTb) (12:26) Ch6_3 Backward Stepwise Selection (YTa, YTb) (5:26) Ch6_4 Estimating Test Error Using Mallow's Cp, AIC, BIC, Adjusted R-squared (YTa, YTb) (14:06) Ch6_5 Estimating Test Error Using Cross-Validation (YTa, YTb) (8:43) Ch6_6 Shrinkage Methods and Ridge Regression (YTa, YTb) (12:37) Ch6_7 The Lasso (YTa, YTb) (15:21) Ch6_8 Tuning Parameter Selection for Ridge Regression and Lasso (YTa, YTb) (5:27) Ch6_9 Dimension Reduction (YTa, YTb) (4:45) Ch6_10 Principal Components Regression and Partial Least Squares (YTa, YTb) (15:48) Ch6_11 Lab1: Best Subset Selection (YTa, YTb) (10:36) Ch6_12 Lab2: Forward Stepwise Selection and Model Selection Using Validation Set (YTa, YTb) (10:32) Ch6_13 Lab3: Model Selection Using Cross-Validation (YTa, YTb) (5:32) Ch6_14 Lab4: Ridge Regression and Lasso (YTa, YTb) (16:34) optional: Interviews with statistics graduate students (Lab Meeting) (YT) (7:44)	Ex. 9 (p. 286/287): Applying various prediction techniques on the College data set Ex. 11 (p. 287/288): Predicting the per capita crime rate from the Boston data set
18.11.	Wiederholung wesentlicher Konzepte, Übungen in R, Fragen der Kursteilnehmer	–	–	–
25.11.	Nichtlineare Modelle, Splines und Generalized Additive Models (GAM)	7 Moving Beyond Linearity	Ch7_1 Polynomial Regression and Step Functions (YTa, YTb) (14:59) Ch7_2 Piecewise Polynomials and Splines (YTa, YTb) (13:13) Ch7_3 Smoothing Splines (YTa, YTb) (10:10) Ch7_4 Local Regression and Generalized Additive Models (YTa, YTb) (10:45) Ch7_5 Lab1: Polynomials (YTa, YTb) (21:11) Ch7_6 Lab2: Splines and Generalized Additive Models (YTa, YTb) (12:15)	Ex. 6 and 7 (p. 323/324): Non-linear prediction using the Wage data set Ex. 8 (p. 324): Non-linear prediction using the Auto data set Ex. 9 (p. 324): Non-linear prediction using the Boston data set Ex. 10 (p. 324/325): Using a GAM on the College data set
2.12.	Decision trees, pruning, Bagging, Random Forests, Boosting	8 Tree-Based Methods	Ch8_1 Decision Trees (YTa, YTb) (14:37) Ch8_2 Pruning a Decision Tree (YTa, YTb) (11:45) Ch8_3 Classification Trees and Comparison with Linear Models (YTa, YTb) (11:00) Ch8_4 Bootstrap Aggregation (Bagging) and Random Forests (YTa, YTb) (13:45) Ch8_5 Boosting and Variable Importance (YTa, YTb) (12:03) Ch8_6 Lab1: Decision Trees (YTa, YTb) (10:13) Ch8_7 Lab2: Random Forests and Boosting (YTa, YTb) (15:35) optional: Interview with Jerome Friedman (YT) (10:29)	Ex. 8 (p. 363): A regression tree on the Carseats data set Ex. 9 (p. 363/364): Regression trees on the OJ data set Ex. 10 (p. 364): Boosting in the Hitters data set Ex. 11 (p. 364/365): Boosting in the Caravan data set
9.12.	Support Vector Machines (SVM)	9 Support Vector Machines	Ch9_1 Maximal Margin Classifier (YTa, YTb) (11:35) Ch9_2 Support Vector Classifier (YTa, YTb) (8:04) Ch9_3 Kernels and Support Vector Machines (YTa, YTb) (15:04) Ch9_4 Example and Comparison with Logistic Regression (YTa, YTb) (14:47) Ch9_5 Lab1: Support Vector Machine for Classification (YTa, YTb) (10:13) Ch9_6 Lab2: Nonlinear Support Vector Machine (YTa, YTb) (7:54)	Ex. 7 (p. 401): Using a SVM on the Auto data set Ex. 8 (p. 401/402): Using a SVM on the OJ data set
16.12.	Deep Learning, Neuronale Netze	10 Deep Learning	Deep learning 1: But what is a Neural Network? (YT) (19:13) Deep learning 2: Gradient descent, how neural networks learn (YT) (21:00) Deep learning 3: What is backpropagation really doing? (YT) (13:53) Deep learning 4: Backpropagation calculus (YT) (10:17) (Quelle: 3Blue1Brown)	–
23.12.	Hauptkomponenten-analyse (PCA) und Clustering (K-means und hierarchisch)	12 Unsupervised Learning	Ch12_1 Unsupervised Learning and Principal Components Analysis (YTa, YTb) (12:37) Ch12_2 Exploring Principal Components Analysis and Proportion of Variance Explained (YTa, YTb) (17:39) Ch12_3 K-means Clustering (YTa, YTb) (17:17) Ch12_4 Hierarchical Clustering (YTa, YTb) (14:45) Ch12_5 Breast Cancer Example of Hierarchical Clustering (YTa, YTb) (9:24) Ch12_6 Lab1: Principal Components Analysis (YTa, YTb) (6:28) Ch12_7 Lab2: K-means Clustering (YTa, YTb) (6:31) Ch12_8 Lab3: Hierarchical Clustering (YTa, YTb) (6:33)	Ex. 9 (p. 550/551) and 13 (p. 552): Hierarchical clustering on the USArrests and Ch12Ex13.csv data sets
13.1.	Fragen der Kursteilnehmer	–	–	–
20.1.	Präsentationen der Kursteilnehmer	–	–	–

Machine Learning mit R – Wintersemester 2026/27

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