Online AI and Data Science Certificate Courses

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. The theoretical aspects of the algorithms will be studied, and assignments will be given to test their applicability.

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.

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.