课程项目
学生将在 KTBlocks 系统上构建、测试和发布自己的游戏项目
编码平台
KTCoder 一体化编码平台支持我们的互动在线课程、专业化课程体系，以及学生对学习的热情。
学生答疑辅导时间
答疑辅导时间由我们高素质的助教团队主导。这是帮你的代码获取即时反馈的免费便捷途径。
进度报告
KTBYTE 将通过电子邮件的方式向家长发送学生的课堂表现和成绩报告
结业证书
学生完成每门课程后均可申请结业证书。
課程描述:
[AI 1]是KTBYTE提供的一门数学课，课程负担重，要求学生掌握自主学习。学生将学习使用建模工具并理解复杂数据集，还会学习通常用于解决“大数据”问题的工具和算法。涵盖的主题包括监督式学习中的不同技巧， 非监督式学习和强化学习。本课程通过Python中的pandas,numpy和sk-Learn学习库进行讲授。学生每周需要完成大概2小时的家庭作业，此外，在学期结束时，需要提交最终项目。[AI 1] 和 核心课：[AI 1]主要是帮助学生理解学习打下理论和数学基础， 此外老师也会布置常规习题集给学生做。课程目标是帮助学生推导和理解各种模型的方程。其中包括聚类，线性回归， 和朴素贝叶斯算法。。对于许多KTBYTE的学生来说，[AI 1]课程将会是他们第一次使用Python进行编程。与核心课程不同，学生不会从头开始学习Python且需要通过课堂示范掌握编程语言。
Research Projects from KTBYTE students
入班要求
修完[CORE 5b]或AP计算机科学，或获得导师同意。还需掌握代数II数学。
Class Description:
[AI 1]是KTBYTE提供的一门数学课，课程负担重，要求学生掌握自主学习。学生将学习使用建模工具并理解复杂数据集，还会学习通常用于解决“大数据”问题的工具和算法。涵盖的主题包括监督式学习中的不同技巧， 非监督式学习和强化学习。本课程通过Python中的pandas,numpy和sk-Learn学习库进行讲授。学生每周需要完成大概2小时的家庭作业，此外，在学期结束时，需要提交最终项目。[AI 1] 和 核心课：[AI 1]主要是帮助学生理解学习打下理论和数学基础， 此外老师也会布置常规习题集给学生做。课程目标是帮助学生推导和理解各种模型的方程。其中包括聚类，线性回归， 和朴素贝叶斯算法。。对于许多KTBYTE的学生来说，[AI 1]课程将会是他们第一次使用Python进行编程。与核心课程不同，学生不会从头开始学习Python且需要通过课堂示范掌握编程语言。
Prerequisites:
修完[CORE 5b]或AP计算机科学，或获得导师同意。还需掌握代数II数学。
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
课程大纲
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