Intro to Machine Learning

Full Course

Class Package

Virtual Machine (VM)

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 Progress Report

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

Class Description:

[AI 1] 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 pandas, numpy, and sk-Learn libraries. Students will have roughly 2 hours of homework assignments per week, plus a final project due at the end of the semester. [AI 1] vs Core classes: [AI 1] 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. For many KTBYTE students, [AI 1] 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.

Prerequisites:

Completion of [CORE 6a] or AP CS, or permission of instructor. Also requires Algebra II math experience.