| Lecture Notes & ML Booklet |
 Lecture notes |
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 Booklet |
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| Week 1: Introduction & semi-supervised binary classification with graphs |
Slides: Introduction |
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Coursework #1 (PDF) |
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 Coursework #1 (Python) |
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Coursework #1 - solution (PDF) |
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Coursework #1 - solution (Python) |
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Lab #1 (PDF) |
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Lecture notes - Semi Supervised (PDF) |
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| Week 2: Unsupervised Binary Classification With Graphs & Clustering |
Slides: Clustering |
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Coursework #2 (Python) |
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Coursework #2 - solution (Python) |
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Lecture notes - Semi Supervised (PDF) |
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| Week 3: K-means Clustering & Gaussian Mixture Models |
Slides: K-means |
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Slides: GMM |
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Coursework #3 (PDF) |
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Coursework #3 - solution (PDF) |
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| Week 4: Spectral Clustering |
 Coursework #4 - solution (zip) |
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Coursework #4 (zip) |
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Lecture notes - Spectral Clustering (PDF) |
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| Week 5: Evaluating Clustering Methods & Intro to Matrix factorisation |
Coursework #5 (PDF) |
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Lab #5 (PDF) |
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Coursework #5 - solution (PDF) |
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Lecture Notes - Evaluation (PDF) |
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| Week 6: SVD & PCA |
Coursework #6 (Python) |
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Coursework #6 - solution (Python) |
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Lab #6 (PDF) |
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Lecture Notes - SVD (PDF) |
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| Week 7: PCA & Robust PCA |
Slides: Robust PCA (PDF) |
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Lecture Notes - PCA (PDF) |
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Coursework #7 (PDF) |
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Coursework #7 - solution (PDF) |
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| Week 8: Robust PCA |
Lecture Notes - Robust PCA (PDF) |
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Slides: Matrix Completion |
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Coursework #8 (Zip) |
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Coursework #8 - solution (ZIP) |
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Coursework #9 (PDF) |
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Coursework #9 - solution (PDF) |
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| Week 9: Review |
Lecture notes - Review (PDF) |
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| Week 10: Matrix Completion & Neural Networks |
Lecture Notes - Matrix Completion (PDF) |
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Lecture Notes - Neural Networks (PDF) |
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| Mid-term exam |
Past exams |
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| Week 12: Autoencoders & PyTorch |
Slides: Autoencoders (PDF) |
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PyTorch Examples (Python) |
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Coursework 10 (Python) |
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Coursework 10 - solution (Python) |
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