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STAT 479: Machine Learning (Fall 2019) Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison Topics Summary (Planned) Below is a list of the topics I am planning to cover. Note that while these topics are numerated by lectures, note that some lectures are longer or shorter than others. Also, we may skip over certain topics in favor of others if time is a concern. While this section provides an overview of

potential topics to be covered, the actual topics will be listed in the course calendar. **Part I: Introduction** Lecture 1: What is Machine Learning? Lecture 2: Intro to Supervised Learning: KNN **Part II: Computational Foundations** Lecture 3: Using Python, Anaconda, IPython, Jupyter Notebooks Lecture 4: Scientific Computing with NumPy, SciPy, and Matplotlib Lecture 5: Data Preprocessing and Machine Learning with Scikit-Learn **Part III: Tree-Based Methods** Lecture 6: Decision Trees Lecture 7: Ensemble Methods **Part IV: Evaluation** Lecture 8

: Model Evaluation 1: Introduction to Overfitting and Underfitting Lecture 9: Model Evaluation 2: Uncertainty Estimates and Resampling Lecture 10: Model Evaluation 3: Model Selection and Cross-Validation Lecture 11: Model Evaluation 4: Algorithm Selection and Statistical Tests Lecture 12: Model Evaluation 5: Performance Metrics **Part V: Dimensionality Reduction** Lecture 13: Feature Selection Lecture 14: Feature Extraction