Introduction to machine learning

• What is machine learning? (10 min) [slides]
• Supervised and unsupervised learning (6 min) [slides]
• Practical notebook: Python and NumPy [colab]

Linear regression

• Simple linear regression (14 min) [slides]
• Vector and matrix derivatives (13 min) [slides]
• Multiple linear regression - Model and loss (16 min) [slides]
• Multiple linear regression - Optimisation (8 min) [slides]
• Polynomial regression and basis functions (15 min) [slides]
• Overfitting (10 min) [slides]
• Regularisation (15 min) [slides]
• Evaluation and interpretation (11 min) [slides]
• Practical notebook: Linear regression [colab]

Training, validating, testing

• Training, validating and testing (18 min) [slides]

Gaussians

• Maximum likelihood estimation (20 min) [slides]
• Multivariate Gaussian distribution (5 min) [slides]

Classification

• Task (9 min) [slides]
• K-nearest neighbours (15 min) [slides]
• Bayes classifier and naive Bayes (17 min) [slides]
• Generative vs discriminative (8 min) [slides]

Logistic regression

• Model and loss (14 min) [slides]
• Gradient descent - Fundamentals (11 min) [slides]
• Optimisation (7 min) [slides]
• The decision boundary and weight vector (21 min) [slides]
• Basis functions and regularisation (6 min) [slides]
• Multiclass - One-vs-rest classification (5 min) [slides]
• Multiclass - Softmax regression (15 min) [slides]

Preprocessing

• Feature normalisation and scaling (14 min) [slides]
• Categorical features and categorical output (9 min) [slides]

Classification evaluation

• Accuracy, precision, recall, F1 (18 min) [slides]
• Precision, recall example (10 min) [slides]
• Practical notebook: Classification [data1, data2, data3, colab]

Decision trees

• Intro - Decision trees for classification (10 min) [slides]
• Intro - Regression trees (12 min) [slides]
• Regression trees - Model (11 min) [slides]
• Regression trees - Algorithm (18 min) [slides]
• Regression trees - Tree pruning (9 min) [slides]
• Decision trees - Classification (7 min) [slides]
• Decision trees - Algorithm (16 min) [slides]
• Decision trees - In practice (8 min) [slides]
• Practical notebook: Decision trees [data, colab]

Ensemble methods

• Bagging (13 min) [slides]
• Random forests (7 min) [slides]
• Boosting for regression (21 min) [slides]
• AdaBoost for classification - Setup (10 min) [slides]
• AdaBoost for classification - Step-by-step (15 min) [slides]
• AdaBoost for classification - Details (11 min) [slides]

K-means clustering

• Introduction to unsupervised learning (19 min) [slides]
• K-means clustering - Algorithm (16 min) [slides]
• K-means clustering - Details (14 min) [slides]
• Practical notebook: Clustering [data, colab]

Principal components analysis

• Introduction (16 min) [slides]
• Mathematical background (7 min) [slides]
• Setup (17 min) [slides]
• Learning (19 min) [slides]
• Minimising reconstruction (7 min) [slides]
• Relationship to SVD (9 min) [slides]
• Steps (6 min) [slides]
• Practical notebook: Dimensionality reduction [colab]

Introduction to neural networks

Full playlist

• Neural network preliminaries: Vector and matrix derivatives (5 min) [slides]
• Neural network preliminaries: The chain rule for vector derivatives (7 min) [slides]
• Neural network preliminaries: Gradient descent (4 min) [slides]
• Neural network preliminaries: Logistic regression, softmax regression and basis functions (6 min) [slides]
• From logistic regression with basis functions to neural networks (19 min) [slides]
• Why is it called a neural network? (4 min) [slides]
• Backpropagation (without forks) (31 min) [slides]
• Backprop for a multilayer feedforward neural network (4 min) [slides]
• Computational graphs and automatic differentiation for neural networks (7 min) [slides]
• Common derivatives for neural networks (7 min) [slides]
• A general notation for derivatives (in neural networks) (8 min) [slides]
• Forks in neural networks (14 min) [slides]
• Backpropagation in general (now with forks) (4 min) [slides]
• What is the difference between negative log likelihood and cross entropy? (in neural networks) (9 min) [slides]
• Neural networks in practice (7 min) [slides]
• Neural networks examples: Natural language processing (8 min) [slides]
• Embedding layers in neural networks (10 min) [slides]
• What should I read to learn about neural networks? (3 min) [slides]

Lecture notes

I use these notes when presenting the above material in in-person lectures.

• Introduction to machine learning
• Simple linear regression
• Multiple linear regression
• Vector and matrix derivatives
• Polynomial regression and basis functions
• Overfitting and regularisation
• Regression: Evaluation and interpretation
• Training, validating and testing
• Probability refresher
• Maximum likelihood estimation
• Multivariate Gaussian distribution
• Classification
• Gradient descent
• Binary logistic regression
• Multiclass logistic regression
• Preprocessing: Normalisation, scaling and categorical data
• Classification evaluation
• Introduction to trees
• Ensemble methods
• Introduction to unsupervised learning
• K-means clustering
• Principal components analysis
• Introduction to neural networks
• Revisiting supervised and unsupervised learning

Acknowledgements

These videos are heavily inspired by three courses: the MLPR course taught by Iain Murray at the University of Edinburgh; the machine learning course taught by Greg Shakhnarovich at TTI-Chicago; and the Coursera machine learning course taught by Andrew Ng. I also consulted the textbook An Introduction to Statistical Learning, especially for examples.

License

Herman Kamper, 2020-2024
This work is released under a Creative Commons Attribution-ShareAlike license (CC BY-SA 4.0).