Intro to Machine Learning
[AI 1]
Full Course
课程套餐
课程介绍:
[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 from KTBYTE students and alumni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。
相关课程
项目示例
These are examples of projects that students create as they grow their skills in [AI 1]
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
所有课程时间
- 即将开始的课程 ▼
- 正在进行的课程 ▼
Summer Semester: Once Per Week
** KTBYTE可以自行决定更改目前安排的授课老师
这些时间都不合适吗？
* Press the plus button to add more availabilities.