Closed-Loop Optimization in Laboratory Automation
Building systems that learn from experimental results and automatically improve processes—covering optimization algorithms, feedback architectures, and practical implementation patterns.
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Machine Learning 17 posts
Instrument Agents: AI Systems That Control Lab Equipment
Designing AI agents that can understand, plan, and execute laboratory instrument operations—covering agent architectures, tool abstraction patterns, and safety boundaries.
Text-to-Protocol: From Natural Language to Executable Lab Procedures
Designing systems that convert natural language instructions into structured, validated laboratory protocols—covering representation formats, LLM pipelines, and safety verification.
AI in Laboratory Automation: Current State, Limitations, and the Path Forward
A comprehensive look at where AI stands in lab automation today—the promising advances, the persistent challenges, and the gap between research demos and production-ready systems.
Building AI-Ready Data Infrastructure in Industrial Software
A practical guide to collecting, structuring, and leveraging data from distributed industrial systems—where each PC runs different environments and logs are your only starting point.
Large Language Models in Lab Automation: From Natural Language to Robot Control
Exploring how LLMs are transforming laboratory automation—from interpreting human commands to orchestrating robotic workflows—and the practical considerations for deployment in air-gapped environments.
Bringing Vision-Language Models to Lab Automation: Challenges and Possibilities
Exploring how VLMs could transform laboratory automation, and the practical constraints of deploying AI in air-gapped industrial environments.
Implementing AI Services in Offline Industrial Environments
A practical guide to deploying AI capabilities on air-gapped systems—from local inference engines to edge-optimized models and hybrid architectures.
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.
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