לא מוכר.
מצו”ב הסילבוס:
Basic tools: Python, Jupyter, numpy, Pandas, sklearn
Types of learning (Supervised\Unsupervised\Reinforcement),
types of features, cost functions,
optimization algorithms (gradient descent)
Linear Regression + Supervised learning:k-NN
Logistic Regression
Bias vs Variance trade-off, model complexity, cross-validation, regularization, feature selection
SVM, kernels
Decision Trees Error metrics: accuracy, precision-recall
Unsupervised learning: clustering (k-means, agglomerative), dimensionality reduction (PCA)
Generative learning algorithms (Naive Bayes)
Ensemble Learning (Bagging, Boosting, Stacking)
Deep learning 1
Deep learning 2
Deep learning 3