# stat479-machine-learning-fs18
**Repository Path**: mirrors_rasbt/stat479-machine-learning-fs18
## Basic Information
- **Project Name**: stat479-machine-learning-fs18
- **Description**: Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-11
- **Last Updated**: 2026-05-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 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](01_overview): What is Machine Learning? An Overview.
- [Lecture 2](02_knn): Intro to Supervised Learning: KNN
**Part II: Computational Foundations**
- [Lecture 3](03_python): Using Python, Anaconda, IPython, Jupyter Notebooks
- [Lecture 4](04_scipython): Scientific Computing with NumPy, SciPy, and Matplotlib
- [Lecture 5](05_sklearn): Data Preprocessing and Machine Learning with Scikit-Learn
**Part III: Tree-Based Methods**
- [Lecture 6](06_trees): Decision Trees
- [Lecture 7](07_ensembles): Ensemble Methods
**Part IV: Evaluation**
- [Lecture 8](08_eval-intro): Model Evaluation 1: Introduction to Overfitting and Underfitting
- [Lecture 9](09_eval-ci): Model Evaluation 2: Uncertainty Estimates and Resampling
- [Lecture 10](10_eval-cv): Model Evaluation 3: Model Selection and Cross-Validation
- [Lecture 11](11_eval-algo): Model Evaluation 4: Algorithm Selection and Statistical Tests
- [Lecture 12](12_eval-metrics): Model Evaluation 5: Performance Metrics
**Part V: Dimensionality Reduction**
- [Lecture 13](13_feat-sele): Feature Selection
- [Lecture 14](14_feat-extract): 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](./other/dl-course-info.md).
**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!
