Applied Artificial Intelligence - 565

Programming for Artificial Intelligence
Describe and implement basic data structures, algorithms, object-oriented programming techniques, and array programming techniques. Design and code solutions for problems related to artificial intelligence. Develop programs using a variety of tools and coding libraries available for artificial intelligence.

Foundations of Artificial Intelligence
Investigate the conceptual foundations of artificial intelligence, including the history of AI, core concepts, approaches, and applications. Discuss principles of project management for artificial intelligence system development. Implement foundational artificial intelligence algorithms through both programming and no-code simulations.

Data Management for Artificial Intelligence
Apply technologies, programming techniques, and statistical methods to build data analytics solutions, encompassing data collection to visualization. Investigate various methods to analyze and interpret data, and to make inferences and predictions. Develop code to read and pre-process data from structured and unstructured formats and to prepare it for machine learning algorithms.

Machine Learning 1
Investigate the conceptual foundations of supervised, unsupervised, and reinforcement learning. Configure, train, test, and deploy machine learning algorithms and artificial neural networks for supervised classification and regression. Evaluate the performance of machine learning algorithms.

The Social Impact of Artificial Intelligence
Discuss the history, current impact, and possible futures of artificial intelligence technology. Describe ethical standards, laws, and policies pertaining to data collection and artificial intelligence. Evaluate ethical issues in artificial intelligence, including issues of bias and representation and the impact of artificial intelligence on labor. Explain various vulnerabilities of machine learning systems and their implications in real-world applications.

Machine Learning 2
Develop, deploy, and evaluate state-of-the-art machine learning solutions, including ensembles, sequential data predictors, reinforcement learning, and transfer learning. Report and reflect on the development of machine learning solutions in real-world applications. Implement solutions involving transfer learning and deep neural networks. Apply machine learning and neural network models to predictive tasks in a variety of knowledge domains, including image classification, medical diagnosis, and economic forecasting. Investigate a problem and recommend the best machine learning tools.

Machine Vision
Develop artificial intelligence solutions in a realistic lab environment for image processing and manipulation, feature extraction, detection, tracking. Implement deep neural networks and transfer learning for image labelling, image restoration, and image generation.

Natural Language Processing
Implement machine learning, artificial neural networks, and transfer learning for tasks such as sentiment analysis, topic classification, translation, and natural language generation using large real-world data sets and model. Use prompt engineering techniques with generative models to build applications. Construct vector representations of text documents for use in artificial intelligence systems. Apply various tools and technologies to efficiently build an intelligent system using a chain of language models.

Artificial Intelligence on the Internet of Things
Implement data collection for machine learning using internet-connected sensors and other wireless devices. Apply machine learning to data collected and stored in edge devices. Design artificial intelligence systems for predictive maintenance and other industrial applications in a realistic setting.

Artificial Intelligence System Design
Design and construct high-performance pipelines that integrate best practices and tools for deploying, monitoring, and maintaining machine learning models in production. Investigate and apply principles of project management, design thinking, and system thinking for artificial intelligence application development. Describe techniques for scaling up artificial intelligence systems. Work with a team to develop a high-level design, project plan, and maintenance plan for a scalable system that addresses a real-world artificial intelligence use case.

AI Capstone
Integrate and apply acquired knowledge of artificial intelligence, principles, and techniques to develop an innovative intelligent system. Analyze and report a detailed list of project’s requirements. Design and implement the solution with a focus on robustness and scalability, and quality assurance. Apply project management principles to create an effective process model, ensuring the identification and resolution of quality-related challenges, and addressing a realworld problem with a customer-centric approach. Prepare the project documentation for analyzing requirements, designing, implementation and testing.