What Will Learn?

• Course Aims

  • Understand the basic principles and algorithms of machine learning. • Apply machine learning methods to real-world energy data and systems. • Develop models to predict energy demand, optimize renewable energy production, and manage grid loads. • Use tools like Python, TensorFlow, and Scikit-Learn for implementing machine learning solutions. • Analyse the challenges and opportunities of integrating machine learning with renewable energy technologies.

Requirements

CSE141

Description

• English Description
  
  This course introduces students to the fundamental concepts of machine learning (ML) and its applications in different sectors including energy systems and renewable energy technologies. Students will learn how to build, train, and evaluate machine learning models that can analyze and predict energy consumption patterns, optimize energy distribution, and manage renewable energy resources. Through theoretical lectures and practical programming sessions, the course will cover supervised and unsupervised learning techniques, including regression, classification, clustering, and decision trees, with a special focus on applications in energy management, renewable forecasting, smart grids, and energy storage systems.