KTBYTE Computer Science Academy >> Classes >>


Statistical Learning

Class Description:

CS82 is a math heavy course offered at KTBYTE, and require students to have mastered self-guided learning. Students will learn tools to model and understand complex data sets, tools and algorithms that are commonly used for tackling "Big Data" problems. Covered topics include different techniques in supervised learning, unsupervised learning and reinforcement learning. This course is taught in Python using the numpy and sk-Learn libraries. Students will have roughtly 2 hours of homework assignments per week, plus a final project due at the end of the semester.

CS82 vs CS0*: CS82 provides the theoretical and mathematical foundations to understand learning, and students do regular problem sets. The goal is to derive and understand the actual equations of various models. This includes techniques such as clustering, linear regression, and naive bayes. However, students are also expected to master basic statistics, which includes computing standard deviations and picking what type of standard model to fit. For many KTBYTE students, CS82 is also the first time they program using python. Unlike core classes, students are not taught python 'from the ground up', and are expected to pick up the language as it is used with examples in class.

Online Class Icon
This is an Online Class

Completion of CS01b or AP CS, or permission of instructor. Also requires Algebra II math experience.

Class Features:

Weekly Problem Sets

Our homework is done online
Virtual Machine (VM)

Included for Online Classes

A Virtual Machine is a remote desktop that allows students to connect to it from anywhere. We provide VMs so that students use it during classes and to work on homework.
Student Support:
Class Recordings

We provide recordings of our classes for if a student misses or needs to re-listen to a class.
Parent Support:
Student Progress Report

The parent account dashboard allows for parents to track their student's progress in the class.

Linear Regression

Online Class Options: