Class Projects
Students will build, test, and publish their own game in KTBlocks
CODING PLATFORM
The KTCoder all-in-one coding platform supports our interactive online classes, our specialized curriculum, and our students’ passion for learning.
STUDENT HELP HOURS
Help hours are led by our highly qualified teaching assistants. It is an easy and free way to get immediate feedback on your code.
PROGRESS REPORTS
KTBYTE will e-mail parents with behavior and grade progess reports.
COMPLETION CERTIFICATES
Students can request a certificate of completion once they finish each course.
Class Description:
[AI 1] is a math heavy course offered at KTBYTE, and require students to have mastered independently following up on lecture topics and self-teaching outside of class. 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.
Research Projects from KTBYTE students
Prerequisites
Completion of [CORE 6a] or AP CS, or permission of instructor. Also requires Algebra II math experience.
Class Description:
[AI 1] is a math heavy course offered at KTBYTE, and require students to have mastered independently following up on lecture topics and self-teaching outside of class. 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.
Related Classes
Syllabus:
Working With Data: Finding Statistics
Importing data sets and finding statistics
Working with Data: Slicing and Indexing
Slicing and indexing data sets
Classification: Types of Problems and Models
What types of problems and models exist in machine learning? What do most models have in common?
Regression: Linear Regression
Linear Regression and Feature Importance
Regression: Common Regression Problems
Types of regression models and what each one is typically used for.
Regression: Gradient Descent
How does gradient descent work, and how can we use it to optimize our models?
Classification: Logistic Regression
Logistic regression
Classification: Decision Trees
Decision trees and feature importance
Classification: More Decision Trees and Random Forest
More on decision trees and using ensemble methods to improve performance
Clustering
Clustering models and unsupervised learning
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
Syllabus
Working With Data: Finding Statistics
Importing data sets and finding statistics
Working with Data: Slicing and Indexing
Slicing and indexing data sets
Classification: Types of Problems and Models
What types of problems and models exist in machine learning? What do most models have in common?
Regression: Linear Regression
Linear Regression and Feature Importance
Regression: Common Regression Problems
Types of regression models and what each one is typically used for.
Regression: Gradient Descent
How does gradient descent work, and how can we use it to optimize our models?
Classification: Logistic Regression
Logistic regression
Classification: Decision Trees
Decision trees and feature importance
Classification: More Decision Trees and Random Forest
More on decision trees and using ensemble methods to improve performance
Clustering
Clustering models and unsupervised learning
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