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Introduction to Data 科学
概述
CEA CAPA Partner 机构: Universidad Carlos III de 马德里
地点: 马德里,西班牙
Primary Subject Area: Computer 科学
指令: 英语
课程代码: 16475
Transcript Source: Partner 机构
课程详细信息: 100级
Recommended Semester Credits: 3
联系时间: 42
描述
1. The Importance of Data 科学
2. Understanding the Data: Case Studies of Exploratory Data Analysis and 视觉ization Techniques I
3. Understanding the Data: Case Studies of Exploratory Data Analysis and 视觉ization Techniques II
4. Importance of a good design of the experiment and choice of performance measures: precision, 灵敏度, 特异性, ROC曲线. 过度学习.
5. Introduction to unsupervised techniques: case studies of clustering I
6. Case studies of clustering II
7. Introduction to unsupervised classification: case studies on decision trees and random forests.
8. Case studies on data reduction techniques (Principal Component Analysis, Independent Component Analysis, Fisher Discriminant Analysis).
9. Introduction to Regression: Case Studies of Linear Regression.
10. Case studies of Logistic Regression.
11. Case studies on probabilistic models.
12. Introduction to the state of the art: case studies on Support vector machines.
13. Case studies on Deep Learning.
2. Understanding the Data: Case Studies of Exploratory Data Analysis and 视觉ization Techniques I
3. Understanding the Data: Case Studies of Exploratory Data Analysis and 视觉ization Techniques II
4. Importance of a good design of the experiment and choice of performance measures: precision, 灵敏度, 特异性, ROC曲线. 过度学习.
5. Introduction to unsupervised techniques: case studies of clustering I
6. Case studies of clustering II
7. Introduction to unsupervised classification: case studies on decision trees and random forests.
8. Case studies on data reduction techniques (Principal Component Analysis, Independent Component Analysis, Fisher Discriminant Analysis).
9. Introduction to Regression: Case Studies of Linear Regression.
10. Case studies of Logistic Regression.
11. Case studies on probabilistic models.
12. Introduction to the state of the art: case studies on Support vector machines.
13. Case studies on Deep Learning.
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