Contenu de la matière

I.   General framework: representation, loss function, generalization and over-learning
entropy, supervised / unsupervised
II. Supervised Learning and Linear Regression
III. Classification and Logistic Regression
IV. Decision Tree and Random Forest
V. Naïve Bayes and Support Vector Machine
VI. Unsupervised Learning
VII.   Natural Language Processing and Text Mining
VIII.  Deep Learning
IX. Time Series Analysis