Andrew Moore CMU machine learning Notes（ML part）
2017-09-23 18:51
337 查看
墒
增益
增益率
贝叶斯分类器
朴素贝叶斯
分布规则
高斯分布
MLE
线性回归
线性感知器
线性感知激活
动量方法
交叉验证
最近邻
k邻近
核回归（近邻加权）
局部加权（拟合加权）
高斯混合假设
马尔科夫
描述
三问题
前向迭代层次计算解决概率计算维比特算法动态规划记录最佳路径
模型学习EM算法
EM for HMM
内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理 
相关文章推荐
- Notes - Coursera MachineLearning by Andrew NG - Week1
- Machine Learning Lecture Notes
- Stanford Machine Learning (by Andrew NG) --- (week 9) Anomaly Detection&Recommende
- Android_Learning_Notes_Part 1
- （原创）Stanford Machine Learning (by Andrew NG) --- (week 1) Introduction
- Machine Learning Lecture Notes
- Machine Learning Notes II
- Machine Learning by Andrew Ng --- Logistic Regression of Multi-class Classification
- 【菜鸟学深度】Introduction to Machine Learning CMU-10701
- Andrew Ng Machine Learning 专题【Machine Learning Advice】
- Android_Learning_Notes_Part 2
- Andrew’s machine learning course
- （原创）Stanford Machine Learning (by Andrew NG) --- (week 3) Logistic Regression & Regularization
- Andrew NG <machine learning>week 3,class1
- Machine Learning by Andrew Ng-----note
- Machine Learning by Andrew Ng --- K-means
- Notes about <Oblivious Multi-Party Machine Learning on Trusted Processors>
- Machine Learning Notes - PLA
- 《Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning》译文
- （原创）Stanford Machine Learning (by Andrew NG) --- (week 1) Linear Regression