Basic Concepts:Machine Learning, Probability Theory, Supervised Learning, Unsupervised Learning, Classificazione, Regressione, Training/Validation/Test Set, Performance Evaluation, Data Normalization, Overfitting and Regularization, XOR Problem, Perceptron
Linear Models for Regression:Linear Regression, Gradient descent algorithm,Learning Rateand Debugging, Polynomial Regression,Overfitting e Regularization for Linear Regression,Regressione Lineare in Pytorch, API ad oggetti di Pytorch, Monitoring del training mediante Visdom,Linear Regression Evaluation, Learning Algorithms Design
Linear Models for Classification:Logistic Regression,Logistic Regression with Pytorch, Regularization for Logistic Regression, Stocastic Gradient Descent, Momentum for Gradient Descent, Multi-Class Classification, Classifiers Evaluation
Softmax Classifier and python implementation
Computational Graphs and Backpropagation
Neural Networksand python implementation
Deep Learning: Convolutional Neural Network and python implementation, Transfer Learning
Advancet Deep Learning: Introduction to LSTM, Autoencoders, Metric Learning
Python programming and Python Libraries for Machine Learning

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