Introduction to Applications of Artificial Intelligence

Catalog description

This course introduces Artificial Intelligence (AI) in its contemporary form, and its applications. In this course we will understand how big data is used to create AI models, what is meant by AI modeling and how modeling in AI needs large amounts of data. This reigning paradigm of AI is called learning from data, or data-driven AI, and the specific term for this group of algorithms is Machine Learning (ML). An ML algorithm essentially maps inputs to desired outputs so these algorithms are very useful whenever there is need to automate certain parts of decision making from data. While discussing ML we will understand the main goal of ML which is to start from an initial generic algorithm and to adjust the operations of the algorithm based on the key features of the training dataset to achieve increasingly higher accuracy in input to output mapping. We will a specific type of ML algorithms called Artificial Neural Networks (ANN). Most advancement in contemporary AI is taking place in ANNs which are “deep” because they have many data processing layers and gives them increased ability to accurately map complex inputs to desired outputs. These modern ANNs are Deep Learning (DL) algorithms and DL has been successful in pushing the state of art in application areas such as computer vision, robotics, and natural language processing. We will discuss the most recent innovations: Foundation Models, and Generative AI which include Large Language Models. All topics in the course will be taught with focus on applications that arise from needs in business, healthcare, national security, and defense.