Week 1: Introduction to data science and machine learning; differences between supervised, unsupervised, and reinforcement learning; introduction to probability and statistics; sample and population properties; covariance and correlation matrix
Week 2: Linear regression; model parametrization and fitting; coefficient of determination
Week 3: Logistic regression; implementation of models in Python
Week 4: Overfitting and underfitting; bias-variance tradeoff dealing with overfitting via regularization (ridge regression and LASSO)
Week 5: Confidence intervals and hypothesis testing
Week 6: Nonlinear regression; loss functions; gradient descent algorithm and its variations; Cross validation and hyperparameter tuning
Week 7: Unsupervised learning; singular value decomposition; principal component analysis; clustering algorithms
Week 8: Decision tress; ensembling; random forests
Week 9: Bagging and boosting; gradient-boosted decision trees
Week 10: Introduction to neural networks
Week 11: Feed-forward and convolutional neural networks
Week 12: Recurrent neural networks
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