DS 学习 - 打卡贴【欢迎指教】

希望楼主经常回来更新呀~

新年开始继续打卡， 争取每周打卡一次。

今年目标是熟悉机器学习各种算法。去年的任务大部分没完成， 今年少订点，争取能完成。

公开课
1. Stanford cs229
2. Stanford cs231n
3. Stanford cs224n
4. UC Berkeley CS 294 Deep Reinforcement Learning
. 1point 3acres 5. Coursera Probabilistic graphical models
6. Edx artificial intelligence

参考书目：
1. Elements of Statistical Learning
2. Deep learning book by Goodfellow etc.
3. Artificial intelligence: a modern approach
4. Machine learning: A Probabilistic Perspective
5. Papers

项目：
Kaggle: 2 in computer vision, 2 in NLP, 2 general

今年课没上，书也没看。读了近50篇paper，打了6个比赛，目的为了简历能好看点，效果还是有一些的。
因为基础太弱，看paper收获不是很明显。所以决定好好打基础，不要再眼高手低了。2020年争取把概率统计的基础学一遍，编程基本功也要练。参考了K姐和其他大咖们的贴子，下面订个新年计划

1. 概率
书：Introduction to Probability, 2nd edition, by John N. Tsitsiklis etc.
课程： MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Instructor: Prof. John Tsitsiklis

2. 统计
书： All of Statistics: A Concise Course in Statistical Inference, by Larry Wasserman
课程：MIT 18.650 Statistics for Applications,  Instructor: Prof. Philippe Rigollet

3. A/B testing
课程： Udacity A/B Testing

4. Causality
书： Causality - Models, Reasoning, and Inference, 2nd edition by Judea Pearl
课程：A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera)

5. Leetcode
以前刷到了200题，争取2020年底到400题

Jan. 1-9

Read book "Lean Analytics" Chapter 1-6
Read book "Introduction to Probability" Chapter 1 Sample space and probability