Artificial Intelligence (AI) Technologies

Our Artificial Intelligence (AI) Technologies curriculum is carefully structured to provide a background in the various facets of AI and their applications in current industries.

The curriculum typically begins with core courses that introduce students to the basics of machine learning, statistics of goal seeking, neural networks, deep learning, and generational transformers such as ChatGPT, which are the core technologies underpinning modern AI systems. 

Subject matter also includes a deep dive into using Artificial Intelligence (AI) technologies such as ChatGPT, Google Gemini and many other tools in your day-to-day job functions.  

Much of the core curriculum is dedicated to machine learning, where students become familiar with different training methods such as supervised, unsupervised, and reinforcement learning. Here, classic algorithms like regression, random forests, support vector machines, k-nearest neighbors, and neural networks are studied. Students also gain hands-on experience in implementing these algorithms in Python and its frameworks.

Data capture/handling is another critical aspect, as AI heavily relies on data. The curriculum covers data formats, preprocessing, feature extraction, and data visualization, teaching students how to work with large datasets, clean and organize data, and extract meaningful insights from it. 

Deep learning, a subset of machine learning, is given special focus. Students delve into the workings of neural networks, exploring various architectures like convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data like text and speech. Natural Language Processing (NLP) and computer vision are also application areas covered in the curriculum.