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STAT479: Machine Learning (Fall 2018)

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

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

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

Creative Commons License
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!