https://github.com/rasbt/stat479-machine-learning-fs19
Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison
https://github.com/rasbt/stat479-machine-learning-fs19
Last synced: about 1 year ago
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Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison
- Host: GitHub
- URL: https://github.com/rasbt/stat479-machine-learning-fs19
- Owner: rasbt
- Created: 2019-08-07T07:36:20.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-11-28T00:04:20.000Z (over 5 years ago)
- Last Synced: 2025-03-28T14:11:12.244Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage: http://pages.stat.wisc.edu/~sraschka/teaching/stat479-fs2019/
- Size: 76.7 MB
- Stars: 733
- Watchers: 124
- Forks: 244
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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](http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/#calendar).
**Part I: Introduction**
- [Lecture 1: What is Machine Learning? An Overview.](./01_overview/)
- [Lecture 2: Intro to Supervised Learning: KNN](./02_knn)
**Part II: Computational Foundations**
- [Lecture 3: Using Python, Anaconda, IPython, Jupyter Notebooks](./03_python)
- [Lecture 4: Scientific Computing with NumPy, SciPy, and Matplotlib](./04_sci-python)
- [Lecture 5: Data Preprocessing and Machine Learning with Scikit-Learn](./05_preprocessing-and-sklearn)
**Part III: Tree-Based Methods**
- [Lecture 6: Decision Trees](./06_trees)
- [Lecture 7: Ensemble Methods](./07_ensembles)
**Part IV: Evaluation**
- [Lecture 8: Model Evaluation 1: Introduction to Overfitting and Underfitting](./08_model-eval-1)
- [Lecture 9: Model Evaluation 2: Uncertainty Estimates and Resampling](./09_eval2-ci)
- [Lecture 10: Model Evaluation 3: Model Selection and Cross-Validation](./10_eval3-cv)
- [Lecture 11: Model Evaluation 4: Algorithm Selection and Statistical Tests](./11_eval4-algo)
- [Lecture 12: Model Evaluation 5: Performance Metrics](./12_eval5-metrics)
**Part V: Dimensionality Reduction**
- [Lecture 13: Feature Selection](./13_feat-sele)
- [Lecture 14: Feature Extraction](./14_feat-extract)