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Background

Tools
Datasets

Mathematics of Machine Learning

Why Maths?
Linear Algebra
Calculus
Probability
- Distributions
- SciPy Statistical Objects

Dimensionality Reduction

What is Dimensionality Reduction?
Principal Component Analysis
- How Does PCA Work?
- Interpretation of PCA
t-SNE
- Using t-SNE in Practice

Clustering

Clustering
k-Means Clustering
- k-Means Algorithm
- How Many Clusters Do I Have?
Gaussian Mixture Models
- Build Your Own GMM
Hierarchical Clustering
DBSCAN

Classification

Classifiers
Logistic Regression
- A Toy Example
Support Vector Machines
- Kernel Tricks
Decision Trees
- Random Forests
Comparing Classification Methods

Regression

Dealing With Continuous Variables
Linear Regression
- Multiple Linear Regression
Polynomial Regression
Support Vector Regressors

Probabilistic Modelling

Thinking About Data Probabilistically
Likelihood
Likelihood Sampling
- Markov chain Monte Carlo Sampling
- Using PyMC
Bayes Theorem
- Priors
- Computing the Posterior
Why Does Bayesian Modelling Use Logs?
Bayesian Evidence

Neural Networks

Computers That Act Like Brains
Artificial Neurons
- Multilayer Perceptron
Training
Build Your Own
Recurrent Neural Networks
- Long Short-Term Memory Networks
Convolutional Neural Networks

Advanced Neural Networks

Reinforcement Learning
Generalised Adversarial Networks
- Building a GAN
- Mode Collapse in Training GANs

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By Bristol Scientific Computing (BriSC)

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