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"Learning Machine Learning" Course, Bogotá, Colombia 2019 #LML2019

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Learning Machine Learning Course

Learning Machine Learning Course, Bogotá, Colombia (© Josh Bloom; June 2019)

Following along

To execute the notebooks with all the dependencies installed, we'll use MyBinder. Here's the stable Binder link: Binder

Bleeding edge (might take a long time to build an image): Binder

If those don't work you can follow along using the static notebooks (ie., no kernel environment) at nbviewer site.

Format

4 days of lectures, approximately one per morning and one per afternoon. Intermittent breakout work by participants.

Lecture Outline

Introductions & Course Goals

  • (Lecture 1) Computational and Inferential Thinking
    • Logistics
    • Data Science, Machine learning, and statistics introduction
    • Exploration: visualization & data preparation
    • Featurization, Dirty Data, and Natural Language Processing

Statistical Learning and Prediction

  • (Lecture 2) Bayesian Inference

    • Introduction
    • MCMC, hierarchical Bayes
    • Gaussian Processes
  • (Lecture 3) Supervised Learning

    • Regression: logistic regression, kNN, error estimation
    • Classification: Random Forest & LightGBM

Neural Networks

  • (Lecture 4) Neural Networks

    • Introductory algorithms and frameworks
    • Fully connected networks for regression and classification
  • (Lecture 5) Deep Convolutional Neural Networks

    • imaging classification
    • sequence inferencing/classification
  • (Lecture 6) Generative and Compressive Modeling

    • auto-encoders
    • GANs
    • surrogate emulation

Self-supervised Learning & Novelty Detection (Lecture 7)

  • transfer learning & hyperparameter optimization
  • Clustering approaches
  • Dimensionality reduction
  • Anomaly/Novelty detections with random forests
  • Outlier detection for Timeseries

ML In the Real World

(Lecture 8)

  • Business considerations
  • Deployability, Scaling, and Maintainability
  • Bias, Reproducibility, GDPR, and Ethics in ML
  • What we didn't cover: Reinforcement Learning, etc.

Major Frameworks:

  • Python 3.6 (or 3.7)
  • numpy, scipy, seaborn
  • sklearn
  • keras/tensorflow
  • spacy
  • pymc3