# Posts

## The Ultimate High School Computer Science Sequence: 9 Months In

By Justin Skycak on

The Eurisko sequence started during the summer of 2019 with an initial cohort of 5 high school students, all aged 15-16 years old and entering their junior year (11th grade). The content of this sequence similar to what would be covered in upper-level undergraduate courses (e.g. data structures/algorithms ranging from linked lists & sorting algorithms to graphs & traversals), and some content may even be beyond (e.g. building a machine learning library in Python from the ground up). The students build everything from scratch: for example, instead of using external libraries like numpy or pandas, the students built their regressors and classifiers on top of matrix and dataframe classes that they wrote themselves. Read more...

## Solving Magic Squares Using Backtracking

By Elijah Tarr on

A magic square can be thought of as a matrix with specific rows, columns, and diagonals adding up to the same number, called the magic constant. For an $n \times n$ magic square, the magic constant is Read more...

## Predator-Prey Modeling with Euler Estimation

By David Gieselman on

Predator-prey relationships are common in nature. One species benefits from a high population of the other, while the other species is hurt by a high population of the first species. Read more...

## Linear and Logistic Regression, Part 3: Categorical Variables, Interaction Terms, and Nonlinear Transformations of Variables

This blog post will explore categorical variables, interaction terms, and non-linear transformations variables. All of these topics revolve around linear regression, which is a way of solving for the coefficients of a linear function that best fits a set of data points. If you want to understand how linear regressions work, look at Part 1 and Part 2. Read more...

## Linear and Logistic Regression, Part 2: Fitting the Models

By Colby Roberts on

This is a blog post exploring how to fit linear and logistic regressions. First, note that linear and logistic regressors have different shapes. The shape of linear regression is a line, while the shape of logistic regression is a sigmoid: Read more...

## Linear and Logistic Regression, Part 1: Understanding the Models

By George Meza on

Regression is when you measure specific data points and fit a function to the trend. This can be used to establish connections between known variables and uncertainties, like the probability of a heart attack occurring via known traits. Another example could be determining the perfect amount of something, like the perfect amount of toppings on a pizza. You can relate the amount of toppings with customer satisfaction and determine an average amount of toppings that would lead to best reviews from customers. Read more...