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Course Description

This course requires students to have a prior knowledge of Artificial Intelligence (AI) I & II, as well as a familiarity with the python programming language. The course provides a comprehensive exploration of how models are trained using Machine Learning, with an emphasis on understanding neural networks and how they work when provided with data. Activation functions are discussed as a means of helping the model adjust its weights and biases for optimal solutions. Additionally, factors that can affect model accuracy are considered, along with methods of optimizing the accuracy percentage.

Students also learn how GPUs can be utilized to improve the training of a model. The course revisits sklearn, which was used in AI II, to further discuss classification, forget gates, and Moore's Law. Through presenting results in graphs that expose the trend of the accuracy and loss, students can determine what issues may be present in their model, such as overfitting or underfitting.

Upon completion of the course, students will have gained a deep understanding of how the learned concepts apply in modern technologies. They will be able to apply these concepts to design and develop effective machine learning models with high levels of accuracy, and have the skills to present their results in a clear and concise manner.