Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison

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? An Overview.
• 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

Part VI: Bayesian Learning

• Lecture 15: Bayes Classifiers
• Lecture 16: Text Data & Sentiment Analysis
• Lecture 17: Naive Bayes Classification

Part VII: Regression

• Lecture 18: Intro to Regression Analysis

Part VIII: Unsupervised Learning

• Lecture 19: Intro to Clustering