This course introduces core deep learning techniques for predictive and prescriptive analytics in engineering and business contexts. Students will explore supervised learning with neural network architectures, including feedforward, convolutional, and recurrent neural networks, applied to problems like demand forecasting, resource allocation, and risk assessment. The course emphasizes hands-on experience with TensorFlow and PyTorch, focusing on structured data (numerical and image-based) and theoretical foundations such as optimization and regularization. Practical case studies highlight real-world applications, complementing generative AI techniques covered in other courses.