OPWD

AI Applications February 2022

April 19th 2022 Artificial intelligence (AI) is used to solve problems in research and industry. This course provides students with an understanding of and practical hands-on experience building and using AI systems. Students will obtain the skills and knowledge they need to use AI to solve real-world agricultural and life sciences problems. By the end of this course, students will be able to: -Use Google Colaboratory (Google Colab) and Jupyter Notebooks to build and train neural networks. -Demonstrate a basic understanding of modern AI and the history of AI development, using correct vocabulary to describe the characteristics of neural networks. -Implement multi-neuron layers and multi-layer networks to build general nonlinear neural networks in TensorFlow. -Define overfitting and use AI vocabulary to describe how overfitting is evaluated in practice. -Diagnose model overfitting in TensorFlow using validation data, and implement and evaluate standard methods to mitigate overfitting in TensorFlow. -Identify important applications of phenotype prediction in agricultural and life sciences.

AI in Agricultural and Life Sciences

AI in Agriculture- October 2022

November 2022 Awarded 1.5 CEUs This is a 15-hour hybrid course. Learners will meet synchronously via Zoom and will have asynchronous activities. Learners will have eight weeks upon registration to complete the five modules which will require the students to complete at least three hours of work each week. At the end of the course, students will earn a badge for course completion. Learners will earn 1.5 continuing education units (CEUs). Purpose of the Course: Extracting information and making informed decisions based on data is the primary goal of data analysis. This course will consider various analytical tools, approaches, and methods to aid this goal, including data visualization, regression, classification, high-dimensional data inference, and machine learning. Commonly used data science software R will be introduced and illustrated with examples. By the end of this course, students will be able to: -Apply data science tools and techniques to real-world problems. - Implement data science tools using R.

The pedagogical approach will be asynchronous lectures where participants will be introduced to multiple applications of AI tools/methods to analyze business problems. The focus is to provide insights into how AI tools/methods can be effectively leveraged to make better business decisions. By the end of this course, students will be able to: -Identify key principles related to the business applications of AI in marketing, finance, digital platforms, and operations/supply chain management. -Explain multiple AI-based methods/technologies used to successfully model a variety of business problems. -Describe specific applications of AI to address business problems.

Certificate students receive once they complete the 120 hour pre-license course

Bail Bonds 120-Hour Pre-Licensure

This microcredenital is earned by completing: -Ethics in AI 15h course -Fundamentals in AI 15h course -AI in Business 15h course

Advances in the Internet and Web technologies provide managers with access to vast, unstructured, and dynamic data (popularly called “Big Data”). Understanding, analyzing, and using this data for making better business decisions using AI methods/tools is the primary focus of this course. A team of faculty in the Warrington College of Business at UF will describe, through business applications, the use of these methods/tools for addressing problems in finance (e.g., peer-to-peer lending, smart contracts, asset value prediction, credit-risk assessment, portfolio allocation and performance forecasting), digital platforms (e.g., predicting platform profitability, analyzing product reviews to reveal consumer preferences), marketing (e.g., preventing customer churn, predicting customer behavior to identify omnichannel preferences and behavior, and responses to digital advertising.), and supply chain/operations (e.g., identifying reliable suppliers, demand forecasting, predicting process quality, and operational assessment of third-party channel members). The pedagogical approach will be a combination of asynchronous lectures and synchronous classes where participants will be exposed to a variety of AI tools/methods which can be effectively leveraged to make better business decisions. By the end of this course, students will be able to: -Overview Business Applications of AI in Marketing, Finance, Digital Platforms, and Operations/Supply Chain Management. -Discuss multiple AI-based methods/technologies used to successfully model a variety of business problems. -Describe specific applications of AI to address business problems.