Introduction and motivation
Survey of motivating applications, good and bad
Course plan and assignment structure
Presentation of learning and modelling: Machine learning in a graphics rendering system
Example: Nearest neighbor classification
Parameters and hyperparameters
Training, validation and testing
Partitioning data: Hold out, the bootstrap, K-fold CV, LOOCV, etc.
Performance metrics: Confusion table, accuracy, precision, and recall
Supervised learning 1
Probabilistic classification and regression
Incorporating notions of risk in classification and regression
Bayesian classification: Linear discriminant analysis
Bayesian classification: Quadratic discriminant analysis
Bayesian classification: Naive Bayes
Supervised learning 2
Parameter estimation
Least squares regression
Regularization: LASSO, ridge regression
Bayesian regression
Logistic regression
Unsupervised learning 1
What is unsupervised learning?
The curse of dimensionality
Principal component analysis
Multidimensional scaling
Overview of K-means
Unsupervised learning 2
Hierarchical clustering
Density based clustering
Anomaly detection, outliers (Isolation forest)
Gaussian mixture models
Deterministic or probabilistic clustering
Working with time series
Motivating examples
Transformation between time and frequency domains
Autoregressive modelling
Autoregressive moving average modelling
Data representation and feature engineering
Development of distinctive features
Selection of distinctive features
Joint optimisation of feature engineering and classification
Machine learning pipeline
AutoML tools
Pitfalls with standard methods
Data augmentation and other tricks
The responsibilities of the engineer and user
Interpreting models, explaining decisions
Correlation and causalities: machine learning is not magic
Specialisation: Reinforcement learning (RL)
Overview of applications in reinforcement learning
Fundamentals of reinforcement learning
Q-learning