Expected end-productTraining Resource

Intended goalsIncludes content about knowledge representation, expert systems, ontologies, genetic algorithms, multi-agent systems, NLP, GAN/VAE, computer vision, convolutional neural networks, recurrent networks, word2vec, transformers, machine learning, and ethics in AI In this curriculum, you will learn: Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI). Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch. Neural Architectures for working with images and text. We will cover recent models but may be a bit lacking in the state-of-the-art. Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

PrerequisitesGit (course can be cloned by Git)

Recommended whenYou're interested in deep learning modalities (e.g., PyTorch, TensorFlow) and AI ethics.

ApplicabilityAnything with AI/ML

Certification examNo exam listed