https://github.com/rasbt/stat479-machine-learning-fs18

Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison
https://github.com/rasbt/stat479-machine-learning-fs18

Last synced: 4 days ago
JSON representation

Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison

• Host: GitHub
• URL: https://github.com/rasbt/stat479-machine-learning-fs18
• Owner: rasbt
• Created: 2018-09-06T00:30:56.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-20T23:45:21.000Z (almost 6 years ago)
• Last Synced: 2024-10-22T16:39:41.926Z (12 days ago)
• Language: Jupyter Notebook
• Homepage: http://stat.wisc.edu/~sraschka/teaching/stat479-fs2018/
• Size: 55.1 MB
• Stars: 489
• Watchers: 49
• Forks: 228
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

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! 

![](other/stat479-fs18-awards.jpg)