ML4T: Machine Learning for Trading - OMSCS 2024 Fall
Key ML-for-trading concepts from Georgia Tech OMSCS ML4T (CS 7646): portfolio theory, signals, risk, and evaluation.
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Machine Learning 9 posts
Unsupervised Learning: K-means Clustering and Anomaly Detection
Explore core concepts of unsupervised learning, including K-means clustering, optimization strategies, and how anomaly detection systems are designed and evaluated.
Decision Trees and Ensembles: From Splits to Random Forests and XGBoost
A complete guide to decision trees, covering entropy, information gain, one-hot encoding, regression trees, and ensemble methods like Random Forest and XGBoost.
Practical Advice for Applying Machine Learning
Learn how to make decisions, evaluate models, handle bias and variance, and manage real-world ML workflows with cross-validation, error analysis, and transfer learning.
Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation
Explore how neural networks are trained with gradient descent, softmax, and backpropagation using TensorFlow. Understand activation functions and multiclass classification techniques.
Understanding Neural Networks: From Biology to TensorFlow
A comprehensive guide to neural networks, forward propagation, TensorFlow implementation, and efficient matrix computations.
Logistic Regression: From Sigmoid to Regularization
A comprehensive breakdown of logistic regression, sigmoid function, loss functions, and regularization for classification tasks.
Regression with Multiple Input Variables
Deep dive into multiple linear regression, vectorization, gradient descent, feature scaling, and polynomial regression.
Introduction to Machine Learning
Overview of supervised and unsupervised learning, linear regression, and gradient descent