Earning Criteria

• Artificial Intelligence: Principles and Techniques: The course covers computational approaches for modeling uncertainty and solving decision problems. Topics include search techniques, constraint satisfaction problems, game playing (including alpha-beta pruning), propositional logic, predicate logic, knowledge representation and probabilistic reasoning. It also covers selected advanced topics in Artificial Intelligence.
• Fundamentals of Data Science: This course provides an overview of popular algorithms in machine learning. Topics include supervised learning, linear and polynomial regression, classification algorithms, gradient descent, unsupervised learning, instance-based learning, neural networks, and genetic algorithms and boosting. The course requires some knowledge of artificial intelligence, and good programming skills.
• Fundamentals of Deep Learning: This course presents an introduction to deep learning and its applications. Topics include introduction to neural networks, regularization, convolution neural networks, recurrent neural networks, long short-term memory networks, auto-encoders, transformers, and generative adversarial networks. An applied approach will be used, where students get hands-on exposure to AI techniques through the use of state-of-the-art machine learning software frameworks.