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| # Schedule | ||
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| | Lecture | Topic | Exercise | | ||
| |---------|------------------------------------------------------------------------------------------|----------| | ||
| | 1 | [Course overview and introduction to Machine Learning](lectures/01_intro.md) | - | | ||
| | 2 | [Linear algebra refresher](lectures/02_linalg.md) | - | | ||
| | 3 | [Probability refresher](lectures/02_prob.md) | - | | ||
| | 4 | [Gradient-based optimization](lectures/03_gradopt.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/VisualOptimization/1_VisualOptimization.ipynb) | | ||
| | 5 | [Linear and Logistic regression](lectures/04_linreg.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/BasicTorch/2_BasicPytorch.ipynb) | | ||
| | 6 | [Neural Networks: perceptron, activation functions](lectures/05_nn.md) | [link1](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/BasicTorch/2_BasicPytorch.ipynb) | | ||
| | Lecture | Topic | Exercise | | ||
| |---------|-------------------------------------------------------------------------------------------|----------| | ||
| | 1 | [Course overview and introduction to Machine Learning](lectures/01_intro.md) | - | | ||
| | 2 | [Linear algebra refresher](lectures/02_linalg.md) | - | | ||
| | 3 | [Probability refresher](lectures/02_prob.md) | - | | ||
| | 4 | [Gradient-based optimization](lectures/03_gradopt.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/VisualOptimization/1_VisualOptimization.ipynb) | | ||
| | 5 | [Linear and Logistic regression](lectures/04_linreg.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/BasicTorch/2_BasicPytorch.ipynb) | | ||
| | 6 | [Neural Networks: perceptron, activation functions](lectures/05_nn.md) | [link1](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/BasicTorch/2_BasicPytorch.ipynb) | | ||
| | 7 | [Neural Networks: backpropagation, initialization, and loss functions](lectures/06_nn.md) | - | | ||
| | 8 | [Best practices in training of Machine Learning models](lectures/07_bestpractice.md) | - | | ||
| | 9 | [Advanced solvers: momentum, RMSProp, Adam, greedy training](lectures/08_gradopt1.md) | - | | ||
| | 10 | [UQ in Neural Networks and Mixture Density Networks](lectures/09_mdn.md) | [link1](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/LearningFunction/LearningFunction.ipynb) [link2](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/MixtureDensityNetwork/MDN.ipynb) | | ||
| | 11 | [Introduction to CNNs](lectures/10_cnn.md) | - | | ||
| | 12 | [CNNs Popular Architectutues](lectures/11_cnnarch.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/SaltNet/SaltNet.ipynb) | | ||
| | 13 | [Sequence modelling: basic principles](lectures/12_seqmod.md) | | | ||
| | 14 | [Sequence modelling: architectures](lectures/12_seqmod.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/EventDetection/EventDetection.ipynb) | | ||
| | 15 | [Dimensionality reduction](lectures/13_dimred.md) | | | ||
| | 16 | [Generative modelling and VAEs reduction](lectures/14_vae.md) | | | ||
| | 17 | [GANs](lectures/15_gans.md) | | | ||
| | 17 | [Scientific ML and PINNs](lectures/16_pinns.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/EikonalPINN/EikonalPINN_constant.ipynb) | | ||
| | 17 | [Deep learning for Inverse Problems](lectures/1y_deepinv.md) | | | ||
| | 8 | [Best practices in training of Machine Learning models](lectures/07_bestpractice.md) | - | | ||
| | 9 | [Advanced solvers: momentum, RMSProp, Adam, greedy training](lectures/08_gradopt1.md) | - | | ||
| | 10 | [UQ in Neural Networks and Mixture Density Networks](lectures/09_mdn.md) | [link1](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/LearningFunction/LearningFunction.ipynb) [link2](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/MixtureDensityNetwork/MDN.ipynb) | | ||
| | 11 | [Introduction to CNNs](lectures/10_cnn.md) | - | | ||
| | 12 | [CNNs Popular Architectutues](lectures/11_cnnarch.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/SaltNet/SaltNet.ipynb) | | ||
| | 13 | [Sequence modelling: basic principles](lectures/12_seqmod.md) | | | ||
| | 14 | [Sequence modelling: architectures](lectures/12_seqmod.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/EventDetection/EventDetection.ipynb) | | ||
| | 15 | [Dimensionality reduction](lectures/13_dimred.md) | | | ||
| | 16 | [Generative modelling and VAEs reduction](lectures/14_vae.md) | | | ||
| | 17 | [GANs](lectures/15_gans.md) | | | ||
| | 17 | [Scientific ML and PINNs](lectures/16_pinns.md) | [link](https://github.com/DIG-Kaust/MLgeoscience/blob/main/labs/notebooks/EikonalPINN/EikonalPINN_constant.ipynb) | | ||
| | 17 | [Deep learning for Inverse Problems](lectures/17_deepinv.md) | | |