Award in Machine Learning (Computer Science)

The "Machine Learning" module is designed to equip students with the practical skills and knowledge required to excel in the field of machine learning. In this hands-on course, students will delve into the exciting world of data-driven decision-making, mastering the art of extracting valuable insights and predictions from complex datasets. module, students will:
1. Understand the core principles and concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
2. Be proficient in Python programming, using it as the primary tool for data manipulation, visualization, and machine learning.
3. Develop the ability to preprocess, clean, and transform raw data effectively to prepare it for machine learning tasks.
4. Gain practical experience in implementing a wide range of machine learning algorithms using libraries like scikit-learn, TensorFlow, and Keras.
5. Master data visualization techniques to communicate findings and insights effectively through compelling visualizations.
6. Learn advanced skills in hyperparameter tuning and ensemble learning to optimize machine learning models.
7. Understand the ethical considerations involved in working with data and machine learning algorithms, with a focus on fairness, bias, and privacy.

For pass marks, grading and resist systems please refer to the Grading System at the end of the document. In specific reference to the situation where a student fails a module, they will be given one chance to resit, and if they fail the resit too, they will need to redo the module.