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