CIS 5903 - Practical AI Development & Optimization

Credits: 3
This course equips students with advanced programming skills essential for developing cutting-edge AI solutions, bridging theoretical concepts with practical implementation. Covering state-of-the-art techniques and tools, students will master efficient, scalable coding for complex AI algorithms and models, including data preprocessing, feature engineering, model architecture design, training optimization, and robust evaluation methods. Using Python as the primary language, the course delves deep into popular AI frameworks like TensorFlow, PyTorch, and JAX, along with high-level APIs such as Keras and FastAI. Advanced topics include distributed computing for AI, GPU acceleration, model deployment strategies, MLOps practices, and emerging paradigms like federated learning and edge AI deployment. Through challenging projects and real-world case studies, students will gain hands-on experience in building, optimizing, and deploying AI applications across diverse domains, preparing them to translate AI research into production-ready systems and tackle complex AI engineering challenges in industry or research settings