Course Material for Machine Learning @ SMIA (scienze matematiche per l’intelligenza artificiale)

#	Topic	Resources
	Introduction to Learning
01a	👨🏼‍🏫 Introduction
01b	👨🏼‍🏫 Statistical Learning Theory	On the board
	Mathematical Tools and Unsupervised Learning
02	👨🏼‍🏫 Math recap and Linear Algebra
-	💻 Numpy Tensors
03	👨🏼‍🏫 Eigendecomposition, PCA, 3DMM
-	💻 SVD, PCA, Linear Generative Model
04	👨🏼‍🏫 SVD in high dimension
05	👨🏼‍🏫 Clustering, Gaussian Mixture Models
	Supervised Learning
06	👨🏼‍🏫 Generative Models
07	👨🏼‍🏫 Linear Regression
-	💻 Linear Regression
08	👨🏼‍🏫 Perceptron, Logistic Regression
09	👨🏼‍🏫 Model Selection, Evaluation, Cross-validation
10	👨🏼‍🏫 Neural Nets and Backpropagation
-	💻 Pytorch, Autograd
11	👨🏼‍🏫 Backpropagation
-	💻 3D shapes as neural SDF