Data Science & Artificial Intelligence

Data science and AI are transforming engineering — from predictive asset maintenance to load forecasting, grid optimization, and fault detection. These notes cover the theory and practice of extracting insight and building intelligence from data.

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Core Topics

Statistics & Probability

Descriptive statistics, probability distributions, hypothesis testing, confidence intervals, Bayesian inference, regression.

Data Engineering

Data pipelines, ETL/ELT, data quality, feature engineering, time-series data handling, streaming vs. batch processing.

Machine Learning — Supervised

Linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs, model evaluation and validation.

Machine Learning — Unsupervised

Clustering (k-means, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP), anomaly detection.

Deep Learning

Neural networks, backpropagation, CNNs, RNNs/LSTMs, Transformers, transfer learning, training at scale.

$\mathcal{L}=-\frac{1}{N}{\sum }_{i=1}^N\left[y_i\log {\hat{y}}_i+(1-y_i)\log (1-{\hat{y}}_i)\right]$

Time Series Analysis

ARIMA, seasonal decomposition, Prophet, LSTM for time series. Load forecasting, solar/wind generation forecasting.

AI in Power Systems & Energy

Load forecasting, fault detection, predictive maintenance, optimal dispatch with ML, DER forecasting, grid anomaly detection.

MLOps & Model Deployment

Model versioning, experiment tracking (MLflow), deployment patterns, monitoring drift, retraining pipelines.

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Key Questions These Notes Answer

• How do I build a load forecasting model for a distribution feeder?
• How does anomaly detection work for detecting equipment faults?
• What is the difference between supervised and unsupervised learning?
• How do I deploy a ML model into a production engineering system?
• How do Transformers work and why are they dominant in AI?

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Prerequisites

• Mathematical Foundations — linear algebra, probability, calculus
• Programming Foundations

Connects To

• Agentic AI — LLMs are the foundation of modern agents
• Information Systems — data infrastructure
• Distribution System Operator — analytics applications
• PhD EE — ML for power systems research
• PhD Energy — AI for energy systems