High School/A.P. and college math and statistics courses

We tutor in Algebra I through high school A.P. courses and the first two years of typical college math and statistics curricula. Our examples will involve your finding patterns in real data.  We will frequently also slow things down in such a way that you can know for sure (“prove”) what you are doing is correct.

Tensors

Advanced high school students as well as college students  may have heard of TensorFlow and TensorCPUs.  We can help you make use of tensors in machine learning and other applications whether you are at the high school, college, or professional levels. We do this by helping you understand perspectives on tensors from  algebra, geometry, and modern logic.  At the high school level, you can make use of ideas about tensors as they involve matrices and decompositions of matrices; at this level,  examples include time series data that we decompose into cycles at the levels of years, months, and seasons.   We also make use of a second perspective on tensors that comes from geometry, and specifically differential geometry.  We can show you how the field of deep learning makes heavy use of this perspective in particular.  If you have knowledge of calculus, you can make use of this approach to tensors as well.     At a third level are perspectives from modern, constructive logic in which inference comes about through ideas involving tensor products, tensor sums, and composition.  Overall, no matter what level,  you can enrich your understanding of ideas about learning and reasoning through a look at classical perspectives to these subjects that entail the use of tensors.

Tools for Statistical Machine Learning

We will enrich your understanding of statistical machine learning whether you are at the high school, college, and professional levels.  At the level of high school statistics, we can help you learn a tool with a powerful Graphical User Interface ( GUI ) approach such as Tableau, QlikView, or a SAS.  Simply put, even in advanced statistical learning, GUIs matter.   If your mathematics is at the college level, you can take advantage of languages that make use of statistical approaches that make extensive use of applied linear algebra and that have comprehensive libraries of methods of optimization.  At this level, our preference here is to use the automated learning capabilities that are available in MATLAB, R, advanced SAS, NONNEM, Maple, and Julia.  Finally, when you discover the value of multi-level statistical modeling, you will also want to learn STAN.  At whatever level you are, we will help you move up a level using your own tool of choice for statistical machine learning.