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: What is Machine Learning? An Overview.
- Lecture 2: Intro to Supervised Learning: KNN
- Python, Matplotlib, Jupyter Notebooks
Part II: Computational Foundations
- Python, Matplotlib, Jupyter Notebooks
- NumPy, SciPy, Scikit-Learn
- Data Preprocessing
Part III: Tree-Based Methods
- Decision Trees
- Ensemble Methods
Part IV: Evaluation
- Model Evaluation and Performance Metrics
- Model Selection and Cross-Validation
Part V: Dimensionality Reduction
- Feature Selection
- Feature Extraction
Part VI: Bayesian Learning
- Bayes Classifiers
- Text Data & Sentiment Analysis
- Naive Bayes Classification
Part VII: Regression and Unsupervised Learning
- Regression Analysis
- Clustering
Part VIII: Artificial Neural Networks
- Perceptron
- Adaline & Logistic Regression
- SVM
- Multilayer Perceptron
Part IX: Deep Learning
- Intro to TensorFlow, PyTorch
- Convolutional Neural Networks
- Recurrent Neural Networks
- Training Neural Nets: "Tricks of the Trade"
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