Machine learning with statistics

Statistical basics (approx. 6 days)

Measurement theory basics (population, sample, sample types, measurement, scale levels)

Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standardization, histograms, bar charts, pie charts, line charts, box plots)

Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots, grouped bar charts)

Basics of inductive inferential statistics (probability distributions, normal distribution, sampling distribution of the mean, significance test, null hypothesis test, significance level, effect size, parameter estimation, confidence intervals, error bar charts, power analysis, sample size)

Data preparation and data cleansing with suitable software

Descriptive analysis

Visualization of statistical results

AI-supported analysis and interpretation of statistical results

Methods for comparing two groups (approx. 5 days)

z-test, t-test for one sample

t-test for independent and related samples

Pretest-posttest designs with two groups

Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonett test, significance test for correlations)

Nonparametric methods (Wilcoxon test, sign test, Mann-Whitney test)

Contingency analyses (binomial test, Fisher's exact test, chi-square test, cross-tabulations, measures of association)

Interpretation of test results

AI-supported interpretation of results

Basics of regression analysis (approx. 2 days)

Linear regression

Model interpretation

AI-supported model interpretation

Correlation analysis

Methods for comparing the means of several groups (approx. 3 days)

One-factorial and two-factorial analysis of variance (ANOVA)

Post-hoc analyses

Interpretation of group differences

Multi-factorial analysis of variance (general linear model)

Fixed, random, crossed and nested factors

Multiple comparison methods (Tukey-HSD, Dunnett, Games-Howell)

Interaction analysis

Power analysis for variance analyses

Introduction to Design of Experiments (DoE) (approx. 1 day)

Full factorial and partial factorial experimental designs

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Introduction to Machine Learning and the ML Workflow (approx. 3 days)

Fundamentals, Applications, and Types of Machine Learning

Classification of Modern AI and Machine Learning Methods

Machine Learning Workflow with scikit-learn

Training, validation, and test data

Underfitting, overfitting, and model evaluation

Cross-Validation

Data Preparation and Feature Engineering (approx. 2 days)

Data Quality and Preprocessing

Scaling, Normalization, and Encoding

Feature Engineering and Feature Selection

Handling Missing Values, Outliers, and Imbalanced Datasets

Supervised Learning – Regression and Classification (approx. 5 days)

Regression and Classification Methods

Linear and Regularized Models

Distance-, probability-, and tree-based methods

Ensemble and Boosting Methods

Model Comparison and Hyperparameter Optimization

Evaluation Metrics and Probabilistic Predictions

Feature importance and explainable AI 

Unsupervised Learning (approx. 2 days)

Dimension Reduction and Feature Extraction

Clustering Methods and Evaluation of Clustering Results

Anomaly Detection

Time Series Analysis and Forecasting (approx. 1 day)

Fundamentals and Analysis of Time Series Data

Forecasting Methods and Machine Learning for Time Series

Neural Networks and Modern AI (approx. 2 days)

Fundamentals of Neural Networks and Deep Learning

Machine Learning for Text Data and Natural Language Processing

Foundation Models, Large Language Models, and Generative AI

Deployment of ML models (approx. 1 day)

Storage and deployment of trained models

Development of simple ML applications

Interfaces and platforms for machine learning models

Project work (approx. 4 days)

To consolidate the content learned

Presentation of the project results