DATA 400 - Applied Machine Learning and AI

3 credit hours - This course offers an in-depth exploration of applied machine learning techniques, focusing on both traditional and advanced methods, including Random Forest, Deep Neural Networks (DNNs), and cutting-edge technologies like Large Language Models (LLMs). Designed for students with a solid foundation in data analysis and machine learning, the course emphasizes the practical application of these techniques to solve real-world problems. Students will advance their knowledge of the machine learning lifecycle, including model selection, training, and deployment, while also diving deeper into more complex algorithms and architectures. The course will cover key topics such as classification, dimensionality reduction, model optimization, and the implementation of deep learning techniques using TensorFlow. Prerequisite(s): DATA 241 and DATA 245.