How pascalegourdeau/comp-551 works
This repository is a collection of COMP551 applied machine-learning teaching notebooks, not a single application with a shared execution pipeline. A learner explores the notebooks directly; the README documents their subject areas, but notebook implementations were not sampled, so the graph does not infer code-level calls or dependencies. The groups preserve the documented coverage, including inference, supervised methods, training and evaluation, deep learning, and nonparametric and unsupervised learning.
Main entry points: Gaussian.ipynb Gaussian · MLE_BayesianInference.ipynb MLE & Bayesian · NaiveBayes.ipynb Naive Bayes · LinearRegression.ipynb Linear Regression
Components 19
Inference
- Gaussian
Gaussian.ipynb - MLE & Bayesian
MLE_BayesianInference.ipynb
Supervised Learning
- Naive Bayes
NaiveBayes.ipynb - Linear Regression
LinearRegression.ipynb - Logistic Regression
LogisticRegression.ipynb - Perceptron & SVM
Perceptron_and_LinearSVM.ipynb
Training & Evaluation
- Gradient Descent
GradientDescent.ipynb - Regularization
Regularization.ipynb - Model Selection
ModelSelection.ipynb - Curse of Dimensionality
CurseOfDimensionality.ipynb
Deep Networks
- MLP & Autodiff
MLP.ipynb - Convolutional Networks
ConvNets.ipynb - Recurrent Networks
RNN.ipynb
Other Methods
- Nearest Neighbours
KNN.ipynb - Trees & Forests
DecisionTree.ipynb - K-Means
KMeansClustering.ipynb - EM for Mixtures
EMforGaussianMixture.ipynb - PCA
PCA.ipynb
Other components
- Learner
Connections 18
- Learner → Gaussian: studies
- Learner → MLE & Bayesian: studies
- Learner → Naive Bayes: studies
- Learner → Linear Regression: studies
- Learner → Logistic Regression: studies
- Learner → Perceptron & SVM: studies
- Learner → Gradient Descent: studies
- Learner → Regularization: studies
- Learner → Model Selection: studies
- Learner → Curse of Dimensionality: studies
- Learner → MLP & Autodiff: studies
- Learner → Convolutional Networks: studies
- Learner → Recurrent Networks: studies
- Learner → Nearest Neighbours: studies
- Learner → Trees & Forests: studies
- Learner → K-Means: studies
- Learner → EM for Mixtures: studies
- Learner → PCA: studies