Basic Methods in Machine Learning

In the Basic Methods in Machine Learning course, we delve into the practical application of fundamental machine learning techniques for data analysis using Python. This course is designed for individuals who want to start using machine learning for data analysis, focusing less on traditional statistics and more on predictive modeling to classify data or predict outcomes. The course is taught interactively with live coding using Jupyter Notebook.

By the end of the course, you will be able to confidently select and utilize basic machine learning techniques, effectively interpret your findings, and apply them to real-world scenarios.

Topics:

• Introduction to Machine Learning

• Linear Regression

• Classification Algorithms

  • Logistic regression

  • K-nearest neighbors

  • Decision Trees

• Model Evaluation and Selection

  • Cross-validation

  • Performance metrics

Differences from "Introduction to Statistics" course:

• Emphasis on predictive modeling rather than statistical inference.

• Focus on understanding key ML terminology and practical applications.

• Helmholtz Munich doctoral researchers cannot replace the mandatory course "Introduction to Statistics" with this course.

Methods:

The course consists of theoretical lessons on machine learning tools, how to apply machine learning techniques and how to evaluate the results. Theoretical lessons will be followed by hands-on examples with best-practice solutions in Python.

1. Understand the Fundamentals of Machine Learning

• Define machine learning and its role in data analysis, distinguishing it from traditional statistical methods.
• Interpret the results of machine learning models and effectively communicate their insights for decision-making purposes.

2. Apply Linear Regression for Predictive Modeling

• Implement linear regression techniques to model relationships between variables and make predictions.
• Integrate automatic feature selection methods like shrinkage approaches.

3. Explore Classification Algorithms

• Apply and differentiate between logistic regression, k-nearest neighbors (KNN), and decision trees for classification tasks.
• Interpret the results of classification algorithms.

4. Evaluate and Select Models Effectively

• Use cross-validation techniques to assess model performance and select the best model for a given task.
• Understand and calculate performance metrics such as accuracy, precision, recall, and F1 score to evaluate classification models.

5. Implement Machine Learning Techniques Using Python

• Apply learned machine learning algorithms and techniques in Python to solve real-world data analysis problems.
• Focus on understanding key ML terminology and practical applications.

Programming skills with Python, e.g. course First Steps in Python. Basic understanding of statistical methods, in particular regression analysis, is recommended, e.g. course Introduction to Statistics.