Why Machine Learning?
•Who am I? How Is The Course Structured?
•Python Or R?
•Download Required Materials
•Setting Up The Python Environment
•Installing Required Tools
•Crash Course: Our Jupyter-Environment
•How To Find The Right File In The Course Materials
•Setting Up The R Environment
•Installing R And RStudio
•Crash Course: R and RStudio
•Intro: Vectores in R
•Intro: data.table In R
•Basics Machine-Learning
•What’s A Model?
•Which Problems Is Machine Learning Used For
•Linear Regression
•Intuiton: Linear Regression (Part 1)
•Intuition: Linear Regression (Part 2)
•Intuition Comprehend With Geogebra
•Quiz 1: Check: Linear Regression
•Python: Read Data And Draw Graphic
•Note: Excel
•Python: Linear Regression (Part 1)
•Python: Linear Regression (Part 2)
•R: Linear Regression (Part 1)
•R: Linear Regression (Part 2)
•R: Linear Regression (Part 3)
•R: Linear Regression (Part 4)
•Excursus (optional): Why Do We Use The Quadratic Error?
•Project: Linear Regression
•Intro: Project Linear Regression (Used Car Sales)
•Project Linear Regression
•1 question
•Python: Sample Solution
•R: Sample Solution
•Train/Test
•Intuition: Train / Test
•Check: Train / Test
•Python: Train / Test (Part 1)
•Python: Train / Test (Part 2)
•Python: Train / Test – Challenge
•R: Train / Test (Part 1)
•R: Train / Test (Part 2)
•R: Train / Test – Challenge
•Linear Regression With Multiple Variables
•Intuition: Linear regression with multiple variables (Part 1)
•Intuition: Linear regression with multiple variables (Part 2)
•Check: Linear regression with multiple variables
•Python: Linear regression with multiple variables (Part 1)
•Python: Linear regression with multiple variables (Part 2)
•R: Linear regression with multiple variables (Part 1 + 2)
•Compare models: coefficient of determination
•Intuition: R² – The coefficient of determination (Part 1)
•Intuition: R² – The coefficient of determination (Part 2)
•Check: R² / coefficient of determination
•Python: Calculate R²
•Python: Compare models by R²
•R: Calculate R²
•R: Compare models by R²
•Practical project: Coefficient of Determination
•Introduction: Practical project: coefficient of determination
•Note: Where can you find the project?
•Python, practical project: Calculate coefficient of determination
•R, Praxisprojekt: Bestimmtheitsmaß berechnen
•Concept: Types of data and how to process them
•Intuition: Data Types (Part 1) – What Types Are There?
•Intuition: Data Types (Part 2) – Metric & Nominal Data
•Intuition: Data Types (Part 3) – Ordinal Data
•Python: Processing Nominal Data (Part 1, Preparing Data)
•Check your solution!
•Python: Processing Nominal Data (Part 2)
•R: Process nominal data (Part 1 + 2)
•Optional excursus: Why were we allowed to remove a column?
•Polynomiale Regression
•Intuition: Polynomial Regression (Part 1)
•Intuition: Polynomial Regression (Part 2)
•Python: Polynomial Regression (Part 1)
•Python: Polynomial Regression (Part 2)
•R: Polynomial Regression (Part 1)
•Practice Project: Polynomial Regression
•Presentation: Practice Project Polynomial Regression
•Python: Sample Solution: Project Polynomial Regression
•R: Sample Solution: Project Polynomial Regression
•Excursus R: Vectorize calculations in R (matrices, …)
•R: Vectors and matrices
•R: Access elements in vectors
•R: Naming of elements
•R: Matrices
•R: Name matrices
•R: DataTables
•Excursus Python: Vectorize Calculations (Numpy)
•Excursus Python: Why Numpy? (Part 1)
•Excursus Python: Why Numpy? (Part 2)
•Excursus Python: Numpy (Arrays)
•Excursus Python: Numpy (Arrays – Application)
•Excursus Python: Numpy (Matrices)
•Excursus Python: The np.where() function
•More stable test results with K-Fold Cross-Validation
•Intuition: K-Fold Cross-Validation
•K-Fold Cross-Validation
•Python: K-Fold Cross Validation (Part 1)
•Python: K-Fold Cross Validation (Part 2)
•Python: K-Fold Cross Validation (Part 3)
•R: K-Fold Cross Validation (Part 1-3)
•Intuition: Repeated K-Fold Cross-Validation
•Repeated K-Fold Cross-Validation
•Python: Repeated K-Fold Cross-Validation
•R: Repeated K-Fold Cross-Validation
•Practical project: K-Fold Cross-Validation
•Task: K-Fold Cross-Validation
•Python: Sample Solution K-Fold Cross-Validation
•R: Sample Solution K-Fold Cross-Validation
•Statistics basics
•Why do we need statistics basics?
•Intuition: mean vs. median
•Mean value and median
•Python: Calculate mean value & median
•R: Calculate mean value & median
•Intuition: Sample
•Intuition: variance and standard deviation
•Variance and standard deviation
•Expert Knowledge (Optional): Corrected Sample Variance
•Python: Draw Histograms
•R: Draw Histograms
•Project: Statistics basics
•Introduction: Practice project “Statistics Basics”
•Python Excursus: Open and filter data
•R Excursus: Open and filter data
•Python: Sample Solution Project “Statistics Basics”
•R: Sample Solution Project “Statistical Foundations
•Classification
•Intuition: What is classification?
•Presentation: Our example data
•Logistic Regression
•Intuition: Logistic Regression
•Intuition: Logistic Regression (Error Term)
•Python: Display data
•Python: Scale data
•Python: Predict data
•Python: Visualize decision boundary
•Python: Visualize decision boundary (smooth transition)
•Python (optional): How is decision limit visualized? (Part 1)
•Python (optional): How is decision limit visualized? (Part 2)
•Python: Your Classification Template
•R: Display data
•R: Scale data
•R: Visualize decision boundary
•R: Visualize decision boundary (smooth transition)
•R (optional): How is the decision limit visualized?
•R: Calculate accuracy
•R: Your Classification Template
•Practice Project: Detect Breast Cancer
•Python: Task breast cancer project
•Python: Sample solution breast cancer project
•R: Task breast cancer project
•R: Sample solution breast cancer project
•Classification with Several Classes
•Intuition: One-Vs-All, One-Vs-One
•One-Vs-All, One-Vs-One
•Python: One-Vs-All, One-Vs-One
•R: One-Vs-All
•Intuition: Multinomial Logistic Regression
•Python: Multinomial Logistic Regression
•R: Multinomial Logistic Regression
•K-Nearest-Neighbor (KNN)
•Intuition: KNN
•KNN
•Python: KNN
•Python: KNN (effects of k)
•R: KNN
•R: KNN (effects of k)
•R: KNN (Tip: The predict function)
•Practical project: Classifying iris blossom leaves
•Project: Iris (Introduction)
•Python: Task Project “Iris”
•Python: Sample solution “Iris” project
•R: Task Project “Iris”
•R: Sample solution “Iris” project
•Decision Trees
•Intuition: Entropy
•Entropy
•Intuition: Decision Trees
•Further information: Entropy
•Python: Decision Trees
•Python: Visualizing Decision Trees (Part 1)
•Python: Visualizing Decision Trees (Part 2)
•Python: Restricting Decision Trees
•Python: Export Decision Trees
•R: Decision trees
•R: Visualize decision trees (Part 1)
•R: Visualize decision trees (Part 2)
•R: Decision trees (the predict() function)
•R: Restrict decision trees
•R: Export decision trees
•Practical project: Classifying mushrooms
•Task: Classify project mushrooms
•Python: Solutions
•R: Solutions
•Random Forests
•Intuition: Random Forest
•Random Forest
•Python: Random Forest
•R: Random Forest
•Task: RandomForest
•The Bias/Variance Dilemma
•Intuition: Training vs. testing terror
•Intuition: Bias vs. Varianz
•Bias vs. Varianz
•Intuition: Comparison of models with high bias or high variance
•Intuition: Validation curve
•Python: Validation curve
•Python: Task Vali