This applications-focused course provides an exploration of contemporary AI and machine learning (ML) techniques in the context of applications. It covers core concepts like supervised and unsupervised learning, methodologies such as regression, support vector machines, and deep learning, along with popular architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Natural Language Processing (NLP) with a focus on Large Language Models (LLMs) and their use. Generative techniques, such as Generative Adversarial Networks (GANs), and industry tools and platforms are also explored. The course also emphasizes ethical, legal, and regulatory challenges, including algorithmic bias, transparency, job displacement, privacy concerns, and various human factors such skill degradation, and decision making.  Case studies focus on responsible design and current legal issues related to AI. Pre-requisites: Calculus I-II and undergraduate statistics.