Instructor: Sebastian Raschka

Lecture material for the Machine Learning course (STAT 479) at University Wisconsin-Madison. For details, please see the course website at http://pages.stat.wisc.edu/~sraschka/teaching/stat479-fs2018/

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

Due to time constraints, the following topics could unfortunately not be covered:

Part VI: Bayesian Learning

• Bayes Classifiers
• Text Data & Sentiment Analysis
• Naive Bayes Classification

Part VII: Regression and Unsupervised Learning

• Regression Analysis
• Clustering

The following topics will be covered at the beginning of the Deep Learning class next Spring. Tentative outline of the DL course.

Part VIII: Introduction to Artificial Neural Networks

• Perceptron
• Adaline & Logistic Regression
• SVM
• Multilayer Perceptron

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Teaching this class was a pleasure, and I am especially happy about how awesome the class projects turned out. Listed below are the winners of the three award categories as determined by ~210 votes. Congratulations!