Update README.md

README.md

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-# Probabilities and Deep Learning
+# Probabilities and Deep Learning 概率与深度学习
 * ### [When Probabilities meet Neural Networks (PPT) Presentation at Sinovation DeeCamp & Peking University, July 2018, 2018年7月在创新工厂和北大合办的DeeCamp的课件(当概率遇到神经网络)](https://github.com/roboticcam/machine-learning-notes/blob/master/DeeCamp2018_Xu_final.pptx) ###
 
 Topics include: Expectation-Maximization & Matrix Capsule Networks; Determinantal Point Process & Neural Networks compression; Kalman Filter & LSTM; Model estimation & Binary classifier 主题包括：EM算法和矩阵胶囊网络; 行列式点过程和神经网络压缩; 卡尔曼滤波器和LSTM; 模型估计和二分类问题关系
 
 * ### [Noise Contrastive Estimation, Re-parameterization, Natural Gradients - 噪声对比估计(NCE)，概率参数从新参数化，自然梯度](https://github.com/roboticcam/machine-learning-notes/blob/master/selected_probability.pdf) ###
 Detailed illustration of Noise Contrastive Estimation (detals & derivations), Probability Density Re-parameterization, Natural Gradients  噪声对比估计 (NCE)、概率密度再参数化以及自然梯度的详细说明 
 
-# Video Tutorial to these notes.
+# Video Tutorial to these notes 视频资料
 
 * I recorded about 20% of these notes in videos in 2015 in Mandarin (all my notes and writings are in English) You may find them on [Youtube](https://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q) and [bilibili](https://space.bilibili.com/327617676) and [Youku](http://i.youku.com/i/UMzIzNDgxNTg5Ng)       我在2015年用中文录制了这些课件中约20％的内容 (我目前的课件都是英文的)大家可以在[Youtube](https://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q) [哔哩哔哩](https://space.bilibili.com/327617676) and [优酷](http://i.youku.com/i/UMzIzNDgxNTg5Ng) 下载
 
-# Data Science
+# Data Science 数据科学
 
 * ### [30 minutes introduction to AI and Machine Learning](https://github.com/roboticcam/machine-learning-notes/blob/master/30_min_AI.pptx)
-An extremely gentle 30 minutes introduction to AI and Machine Learning. Thanks to my PhD student Haodong Chang for assist editing
+An extremely gentle 30 minutes introduction to AI and Machine Learning. Thanks to my PhD student Haodong Chang for assist editing - 30分钟介绍人工智能和机器学习， 感谢我的学生常皓东进行协助编辑
 
-* ### [Regression methods](https://github.com/roboticcam/machine-learning-notes/blob/master/regression.pdf) ###
-Classification: Logistic and Softmax; Regression: Linear, polynomial; Mix Effect model **[[costFunction.m]](https://github.com/roboticcam/matlab_demos/blob/master/costFunction.m)** and **[[soft_max.m]](https://github.com/roboticcam/matlab_demos/blob/master/soft_max.m)**
+* ### [Regression methods 回归方法](https://github.com/roboticcam/machine-learning-notes/blob/master/regression.pdf) ###
+Classification: Logistic and Softmax; Regression: Linear, polynomial; Mix Effect model **[[costFunction.m]](https://github.com/roboticcam/matlab_demos/blob/master/costFunction.m)** and **[[soft_max.m]](https://github.com/roboticcam/matlab_demos/blob/master/soft_max.m)** 分类: Logistic回归和Softmax分类; 回归：线性回归，多项式回归; 混合效果模型 (mixed efffects model) **[[costFunction.m]](https://github.com/roboticcam/matlab_demos/blob/master/costFunction.m)** 和 **[[soft_max.m]](https://github.com/roboticcam/matlab_demos/blob/master/soft_max.m)**
 
 * ### [Recommendation system](https://github.com/roboticcam/machine-learning-notes/blob/master/recommendation.pdf) ###
 collaborative filtering, Factorization Machines, Non-Negative Matrix factorisation, Multiplicative Update Rule
@@ -51,7 +51,7 @@ basic knowledge in reinforcement learning, Markov Decision Process, Bellman Equa
 basic knowledge in Restricted Boltzmann Machine (RBM)
 
 
-# Probability and Statistics Background
+# Probability and Statistics Background 
 
 * ### [Bayesian model](https://github.com/roboticcam/machine-learning-notes/blob/master/bayesian.pdf) ###
 revision on Bayes model include Bayesian predictive model, conditional expectation