/stat479-machine-learning-fs18

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

Primary LanguageJupyter Notebook

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: What is Machine Learning? An Overview.
  • Lecture 2: Intro to Supervised Learning: KNN

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
  • Model Evaluation and Uncertainty Estimates
  • Model Selection and Cross-Validation
  • Model Comparisons with Statistical Tests
  • Performance Metrics

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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