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

Lecture 6: Decision Trees
Lecture 7: Ensemble 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

Lecture 13: Feature Selection
Lecture 14: Feature Extraction

Part VI: Bayesian Learning

Lecture 15: Bayes Classifiers
Lecture 16: Text Data & Sentiment Analysis
Lecture 17: Naive Bayes Classification

Part VII: Regression

Lecture 18: Intro to Regression Analysis

Part VIII: Unsupervised Learning

Lecture 19: Intro to Clustering

satyakam001/stat479-machine-learning-fs19

STAT 479: Machine Learning (Fall 2019)

Topics Summary (Planned)