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  • Class and Course

    Machine Learning for the Energy Industry

    Explore the vast potential of Machine Learning in the energy industry through a comprehensive training program. Grasp essential concepts, methodologies, and techniques of Machine Learning with a strong emphasis on their application in the energy sector. The course is designed to integrate theoretical knowledge with hands-on learning experiences, using Python, the preferred language in the field. As participants, you will not only understand the potential of Machine Learning in transforming the energy landscape but also acquire the practical skills needed to harness its power for strategic advantage. Immerse yourself in this program and innovate the future of the energy industry.

    1. Introduction to  Machine Learning
      • Supervised vs. Unsupervised Learning
    2. Supervised Learning Algorithms: Linear Regression
      • Ordinary Least Squares, Regularization
    3. K-Nearest Neighbors (KNN)
      • Nonlinear, Non-parametric, and Lazy Learning Algorithm
      • KNN for Regression and Classification
    4. Support Vector Machines (SVM)
      • Linear and Non-linear SVMs, SVM for Regression (SVR) and Classification (SVC)
    5. Exercise Session

    1. Decision Trees
      • Basics, Construction Algorithms, Overfitting vs. Underfitting
    2. Ensemble Learning: Bagging and Boosting
      • Random Forest, Boosting Techniques
    3. Exercise Session
      • Decision Tree and Random Forest Regression and Classification
    4. Dimension Reduction
      • Principal Component Analysis (PCA)
    5. Outlier Detection
      • Statistical Models, One-Class SVM

    1. Unsupervised Learning: Overview
      • Differences from Supervised Learning, Use Cases
    2. Clustering Algorithms
      • K-means, DBSCAN
    3. Dimension Reduction and Feature Selection
      • Feature Extraction vs. Feature Selection Techniques
    4. Exercise Session

    1. Supervised Feature Selection
      • Filter-based, Wrapper, and Embedded Methods
    2. Unsupervised Feature Selection
      • Techniques like Laplacian Score, UDFS
    3. Unsupervised Learning in Time Series
      • Dictionary-Based Pattern Extraction, Change-Point Detection
    4. Exercise Session

    1. Review and Advanced Supervised Learning Techniques
      • Recap of Major Topics, Deep Dive into Selected Advanced Topics
    2. Advanced Unsupervised Learning Techniques
      • Additional Clustering and Dimension Reduction Methods
    3. Machine Learning Frameworks
      • Overview of Popular Frameworks, Demystifying the Code
    4. Exercise Session
      • Advanced Hands-on Exercises
    5. Q&A and Wrap-Up
      • Open Session for Questions and Clarifications

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