| 1 |
Learning vs data analysis; loss functions |
NumPy refresher: vectors, dot products, simple loss functions (MSE) |
Shift mindset from “data analysis” to “learning” |
| 2 |
Linear algebra refresher; PCA/SVD (R) |
PCA refresher on known dataset; visualize variance directions |
Align notation & geometry, no novelty overload |
| 3 |
Regression as loss minimization |
Linear regression from scratch via loss minimization |
Bridge known regression → learning viewpoint |
| 4 |
Neural networks: neuron & activations |
Single-neuron model: forward pass + activation functions |
First NN contact, zero frameworks |
| 5 |
Backpropagation & gradients |
Manual backprop for 1–2 layer network |
Demystify training mechanics early |
| 6 |
Loss landscapes & optimization behavior |
Gradient descent experiments: learning rate, conditioning |
Understand why training fails or succeeds |
| 7 |
Generalization, bias–variance |
Overfitting demo: polynomial vs NN models |
Make generalization tangible |
| 8 |
Probabilistic view of learning |
Noise injection; likelihood vs MSE comparison |
Connect probability to physical data |
| 9 |
Representation learning |
Feature learning vs hand-crafted features (simple NN) |
Prepare Materials Genomics concepts |
| 10 |
Latent spaces & autoencoders |
Autoencoder with framework (PyTorch/Keras) |
First latent-space construction |
| 11 |
Unsupervised objectives revisited |
Clustering vs autoencoder embeddings |
Reframe known clustering methods |
| 12 |
Uncertainty in predictions |
Predictive uncertainty via ensembles / dropout |
Teach model trust, not accuracy |
| 13 |
Physics-informed learning |
Simple constrained NN (penalty-based) |
Bridge MFML → ML-PC PINNs |
| 14 |
Explainability & limits |
Sensitivity analysis & failure case |
Scientific responsibility closure |