Ulteriori informazioni
This book serves as the advanced capstone of
The Triality Series
, covering modern predictive analytics, machine learning architectures, and fully independent, multi-stage research workflows under the theme of simulating intelligence.
Simulating Intelligence
pioneers advanced analytical modeling pathways by eliminating rigid assignments of dependent and independent variables to handle complex, codependent rotational systems. It seamlessly transitions users into professional time-variant and reliability engineering, deploying timeseries processing alongside specialized survival analysis methods. Learners build foundational artificial intelligence architectures using clean, reproducible scripts for neural networks, support vector machines, clustering, and decision trees.
The technical curriculum is anchored by deep, proprietary applications, including a nine-stage clinical tracking suite that utilizes adaptive patient dosage mapping, a literary text-mining platform that evaluates narrative pacing across comparative manuscript drafts, and a twelve-stage environmental research pipeline that integrates open-source NOAA buoy data arrays. By combining high-level predictive modeling with extensive real-world research architectures, this book equips advanced students and quantitative researchers with complete, publication-ready computational toolkits.
Info autore
Rebecca D. Wooten is a mathematician, data analyst, instructor, and curriculum designer with more than three decades of teaching experience in mathematics, statistics, and computer programming. A passionate advocate for hands-on, accessible education, she specializes in data analysis and programming with R, bringing her expertise to Florida Southern College, where she has developed interactive courses renowned for their rigor, creativity, and engagement.
Beyond academia, Rebecca is an innovator in computational applications for real-world challenges. She is also a leader in gamified learning and interactive education. Through her Discord community, Educational Gaming, nearly 200 students and educators collaborate on projects, coding challenges, and discussions, fostering a dynamic learning environment that extends beyond the classroom. Her commitment to bridging disciplines led to the founding of The Pedagogue, LLC, an organization dedicated to researching and developing educational materials that integrate learning across multiple platforms. Rebecca’s creative work extends into the arts through Wooten-Works Productions, and her personal YouTube initiative, where she explores:
One-Dimensional Art: storytelling, poetry, and writing
Two-Dimensional Art: digital imagery and formats such as PSD, PNG, and GIF
Three-Dimensional Art: voxel art, 3D modeling, and gaming environments
She also curates Spooky Hearts, a second YouTube channel where she plays Roblox 3D building games, using virtual spaces to illustrate geometry concepts in engaging and dynamic ways. Throughout
The Triality Series: Data Analysis in R
, Rebecca introduces students to statistical analysis and creative coding projects—ranging from marble simulations and probability games to algorithmic art and interactive animations. The first two books in the series,
Educational Gaming
and
Creative Coding
, highlight her original R-based games and include photographs that celebrate the personal and joyful side of technical work.
For more information about her personal journey, see the second edition of
Shades of Me: Art, Poetry, and Technology
.
Riassunto
This book serves as the advanced capstone of
The Triality Series
, covering modern predictive analytics, machine learning architectures, and fully independent, multi-stage research workflows under the theme of simulating intelligence.
Simulating Intelligence
pioneers advanced analytical modeling pathways by eliminating rigid assignments of dependent and independent variables to handle complex, codependent rotational systems. It seamlessly transitions users into professional time-variant and reliability engineering, deploying timeseries processing alongside specialized survival analysis methods. Learners build foundational artificial intelligence architectures using clean, reproducible scripts for neural networks, support vector machines, clustering, and decision trees.
The technical curriculum is anchored by deep, proprietary applications, including a nine-stage clinical tracking suite that utilizes adaptive patient dosage mapping, a literary text-mining platform that evaluates narrative pacing across comparative manuscript drafts, and a twelve-stage environmental research pipeline that integrates open-source NOAA buoy data arrays. By combining high-level predictive modeling with extensive real-world research architectures, this book equips advanced students and quantitative researchers with complete, publication-ready computational toolkits.
