This course presents an introduction to quantum algorithms designed for solving problems in machine learning and optimization. Fundamental quantum algorithms include quantum data encoding, methods for measuring quantum state overlap, amplitude amplification, and variational quantum eigensolvers. Classical machine learning methods, including linear regression, kernel methods, support vector machines, and neural networks, are examined alongside their quantum counterparts.