| 25-Jan |
Lecture 1: Splines Smoothers and GAMs (part 1) |
Lecture 2: Splines Smoothers and GAMs (part 2) |
Lecture 3: Setup and Review of statsmodels |
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| 1-Feb |
Lecture 4: Splines Smoothers and GAMs (part 3) |
Lecture 5: Unsupervised learning cluster analysis (part 1) |
Lecture 5_5: Smoothers pyGAM csaps |
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R:HW1 |
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| 8-Feb |
Lecture 6: Unsupervised learning cluster analysis (part 2) |
Lecture 7: Bayesian statistics (part 1) |
Lecture 8: Clustering in Python (Lab) |
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R:HW2 - D:HW1 |
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| 15-Feb |
No Lecture (Holiday) |
Lecture 9: Bayesian statistics (part 2) |
Lecture 10: Bayes PyMC3 |
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R:HW3 - D:HW2 |
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| 22-Feb |
Lecture 11: Bayesian statistics (part 3) |
Lecture 12: Bayesian statistics (part 4) |
Lecture 13: Hierarchical Models (Lab) |
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| 1-Mar |
No Lecture (Wellness Day) |
Lecture 14: Π CNNs basics |
Lecture 15: ⍺ CNNs Pooling and CNNs Structure |
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R: HW4 - D: HW3 |
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| 8-Mar |
Lecture 16: ύ Backprop max pooling Receptive Fields and feature map viz |
Lecture 17: λ Saliency maps |
Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning |
Advanced Section 1: Segmentation Techniques |
YOLO |
Unet |
| 15-Mar |
Lecture 19: ς RNNs |
Lecture 20: Π GRUs |
Lecture 21: ⍴ LSTMs |
Advanced Section 2: Recurrent Neural Networks and Reservoir Computing |
R:HW5 - D:HW4 |
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| 22-Mar |
Lecture 22: 💬 Language Modelling NLP 1/4 |
Lecture 23: 🔢 Language Representations NLP 2/4 |
Lecture 24: 🧠 Attention (Transformers I) NLP 3/4 |
Advanced Section 3: Word Embeddings |
R:HW6 - D:HW5 |
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| 29-Mar |
Lecture 25: 🤖 (Transformers II) NLP 4/4 |
No Lecture (Wellness Day) |
Lecture 26: Autoencoder |
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| 5-Apr |
Lecture 27: Variational Autoencoder 1/2 |
Lecture 28: Variational Autoencoder 2/2 |
Lecture 29: GANS 1/2 |
Advanced Section 4: Inference in NN |
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R:HW7 - D:HW6 |
| 12-Apr |
Lecture 30: GANS 2/2 |
Lecture 31: Reinforcement Learning - Basics 1 |
Lecture 32: Reinforcement Learning - Basics 2 |
Advanced Section 5: GANS |
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| 19-Apr |
Lecture 33: Deep Reinforcement Learning |
Module: Lecture Domain |
Module: Problem Background |
Advanced Section:6 Reinforcement Learning |
D:HW7 |
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| 26-Apr |
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| 3-May |
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Reading Period |
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| 10-May |
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Module: Final Presentations |
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Finals Week |
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