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.
Part I: Introduction
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
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Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent