Earning Criteria

• Mathematics for Applied AI (core): This course covers the mathematical principles essential for applied artificial intelligence, focusing on both theoretical foundations and practical applications to data problems. Topics include linear algebra, multivariate calculus, optimization, regression, statistics, PCA, and probability, with computational examples throughout.
• Programming for Applied AI (core): This course teaches programming techniques for AI applications using Python. Topics include programming constructs, data structures, classes, APIs, and machine learning algorithms. Students will learn to analyze data, use AI libraries, create visualizations, implement models, and analyze results.
• Machine Learning Fundamentals and Applications (elective): This course covers key machine learning techniques and algorithms, including supervised and unsupervised learning, classification, regression, neural networks, deep learning, and reinforcement learning. Students will apply these skills to real-world problems in fields like business, healthcare, and cybersecurity, focusing on practical AI applications such as computer vision, NLP, and speech recognition, rather than theory or mathematics.
• Deep Learning and its Applications (elective): This course focuses on deep learning principles and applications. Students will learn to build and use deep neural networks, including feedforward networks, CNNs, RNNs, and transformers. Hands-on applications cover tasks in natural language processing, behavioral analysis, financial analysis, and anomaly detection.
• Intro. to Generative AI (elective): This course provides a theoretical foundation and practical skills for Generative AI. Topics include anatomy of generative models, transformers, GAN, the Diffusion Model for images. Students will be equipped with the skills to leverage state of the art techniques for visual representation, generative text, text to image synthesis and more.
• Data Engineering (elective): This course introduces the ETL pipeline: Extract, Transform, and Load. The course provides students with a technical overview on how to source, prepare, and manage data. Students also will be introduced to Dash and to the principles of NoSQL database systems.