AI in Agricultural and Life Sciences April 19th

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.