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This course covers the applied side of algorithmics in machine learning, with some deep learning and evolutionary algorithms thrown in as well.

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Applied Machine Learning

This course covers the applied side of algorithmics in machine learning, with some deep learning and evolutionary algorithms thrown in as well.

Prerequisites: Design of Algorithms, Algebra 2, Calculus 2, Probability and Statistics

Moshe Sipper’s Cat-a-log of Writings

Some Pros and Cons of Basic ML Algorithms, in 2 Minutes

Additional Resources (Cheat Sheets, Vids, Reads, Books, Software, Datasets)


Syllabus

❖ Math ❖ Python ❖ Artificial Intelligence ❖ Date Science ❖ Machine Learning Intro ❖ Scikit-learn ❖ ML Models ❖ Decision Trees ❖ Random Forest ❖ Linear Regression ❖ Logistic Regression ❖ Linear Models ❖ Regularization: Ridge & Lasso ❖ AdaBoost ❖ Gradient Boosting ❖ AddGBoost ❖ Ensembles ❖ XGBoost ❖ Comparing ML algorithms ❖ Gradient Descent ❖ SVM ❖ Bayesian ❖ Metrics ❖ Data Leakage ❖ Dimensionality Reduction ❖ Clustering ❖ Hyperparameters ❖ Some Topics in Probability ❖ Feature Importances ❖ Semi-Supervised Learning ❖ Neural Networks ❖ Deep Learning ❖ DL and AI ❖ Evolutionary Algorithms: Basics ❖ Evolutionary Algorithms: Advanced ❖ Large Language Models


Topics (according to order of instruction)

(: my colab notebooks, : my medium articles)