Intro to Machine Learning

CS82

Full Course

$1906 USD
Before any discounts or coupons
for 18 hours

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:

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 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. 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. 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.

Research from KTBYTE students and alumni

Prerequisites:

Completion of CS02a or AP CS, or permission of instructor. Also requires Algebra II math experience.

Related Classes

Sample Projects

These are examples of projects that students create as they grow their skills in CS82

Linear Regression

Syllabus

Working With Data: Finding Statistics

Importing data sets and finding statistics

Working with Data: Slicing and Indexing

Slicing and indexing data sets

Classification: Logistic Regression

Logistic regression

Classification: Decision Trees

Decision trees and feature importance

Regression: Linear Regression

Linear Regression + Feature Importance

Regression: Decision Tree Regression

Decision Tree Regression + Feature Importance

Text Data: Tokenizing

CountVectorizer, tokenizing

Text Data: Most Important Words

Decision Tree + Most important words / features

Dimensionality Learning

PCA on a text dataset and visualization of topic modeling

Unsupervised Reduction

Clustering on PCA data + Prediction

Cross Validation

Train test split AUC score, accuracy / precision / recall

Research Project

Finding/starting a project

Research Project

Finding and starting a project

Research Project

Related Works + Experiment Design

Research Project

Results

Research Project

Writing, and related works

Research Project

Writing, Introduction and Abstract

Research Project

Finishing the Research Project

All Class Times

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