AIES: AI, Ethics, and Society
Key AI ethics concepts from Georgia Tech OMSCS AIES (CS 6603): data and privacy, statistical literacy, fairness metrics, and bias mitigation.
Jongmin Lee
Posts
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KBAI: Knowledge-Based AI
Key knowledge-based AI concepts from Georgia Tech OMSCS KBAI (CS 7637): knowledge representation, reasoning strategies, learning, planning, and the ARC-AGI project.
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A Few Months Into Team-Wide AI: What I'm Still Figuring Out
A few months into team-wide AI use, here are the patterns I'm seeing, and the question that keeps coming back about what should stay consistent across the team.
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Understanding How Claude Code Works Under the Hood
A walkthrough of Claude Code's execution lifecycle, from the moment you type a prompt to the final response
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How AI Changed My Development Workflow After Switching Jobs
Moving from a Copilot-only environment to a team that actively uses Claude Code: what changed and what I'm still figuring out
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Building a Windows Installer for Offline Field Deployment
How I introduced a WiX-based Windows installer for deploying field software to air-gapped environments, and the unexpected challenges along the way.
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SDP: Software Development Process
Key software engineering concepts from Georgia Tech OMSCS SDP (CS 6300): lifecycle models, UML, testing, Android development, and team collaboration.
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AI4R: Artificial Intelligence for Robotics
Key robotics AI concepts from Georgia Tech OMSCS AI4R (CS 7638): localization, planning, perception, control, and SLAM.
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ML4T: Machine Learning for Trading
Key ML-for-trading concepts from Georgia Tech OMSCS ML4T (CS 7646): portfolio theory, signals, risk, and evaluation.
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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.
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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.
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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.
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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.
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Understanding Neural Networks: From Biology to TensorFlow
A comprehensive guide to neural networks, forward propagation, TensorFlow implementation, and efficient matrix computations.
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Logistic Regression: From Sigmoid to Regularization
A comprehensive breakdown of logistic regression, sigmoid function, loss functions, and regularization for classification tasks.
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Regression with Multiple Input Variables
Deep dive into multiple linear regression, vectorization, gradient descent, feature scaling, and polynomial regression.
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Introduction to Machine Learning
Overview of supervised and unsupervised learning, linear regression, and gradient descent