有很多未入门，想入门的童鞋很迷茫，不知道如何选学习资料，甚至还有的在抱怨学习资料太少，其实是太多，合适自己的不好找。 对于选择学习资料，楼主有个观点，最优先的原则是“有问题能找到回答的人”，至少符合以下3个条件中的1个：
- 能联系到资料的作者
- 身边有朋友正在看（不久前看过）这些资料
- 经典书籍或官方文档，这样可以保障有最多的人群覆盖
如果童鞋同意上述观点，那么，好消息来啦，以下基础系列文章的作者（韩老师、忆臻兄弟，希望更多小伙伴加入）都是楼主的好朋友，大家从头认真学习，有问题就能得到回答，从此学习路上不再孤单 :-)，现在就一起开始愉快的学习之旅吧！
基础分为两大类别，一类是深度学习，另一类是传统的机器学习。
李航老师的《统计学习方法》，也是忆臻兄弟的主要参考
以下两本书，是阿里的同学们正在看的
https://book.douban.com/subject/2061116/
https://book.douban.com/subject/10758624/
《浅析感知机（三）–收敛性证明与对偶形式以及python代码讲解》
[Getting Started With TensorFlow](Getting Started With TensorFlow)
IBM中国研究院认知计算系列课程从科研和产业相结合的角度，深入浅出地介绍了认知计算和人工智能技术的起源、发展和未来方向，以及机器学习和深度学习的基本、工具和应用。
坦白说，从教学设计的角度，略显不足。
一大波高质量论文，涵盖以下经典副本
以下推荐据说是祖神 Michael Jordan（为啥叫祖神？因为是多位大神 Bengio、吴恩达的老师）为想入他门下的骚年列出的书单
1.) Casella, G. and Berger, R.L. (2001). "Statistical Inference" Duxbury Press.
For a slightly more advanced book that's quite clear on mathematical techniques, the following book is quite good:
2.) Ferguson, T. (1996). "A Course in Large Sample Theory" Chapman & Hall/CRC.
You'll need to learn something about asymptotics at some point, and a good starting place is:
3.) Lehmann, E. (2004). "Elements of Large-Sample Theory" Springer.
Those are all frequentist books. You should also read something Bayesian:
4.) Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC.
and you should start to read about Bayesian computation:
5.) Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer.
On the probability front, a good intermediate text is:
6.) Grimmett, G. and Stirzaker, D. (2001). "Probability and Random Processes" Oxford.
At a more advanced level, a very good text is the following:
7.) Pollard, D. (2001). "A User's Guide to Measure Theoretic Probability" Cambridge.
The standard advanced textbook is Durrett, R. (2005). "Probability: Theory and Examples" Duxbury.
Machine learning research also reposes on optimization theory. A good starting book on linear optimization that will prepare you for convex optimization:
8.) Bertsimas, D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization" Athena.
And then you can graduate to:
9.) Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization" Cambridge.
Getting a full understanding of algorithmic linear algebra is also important. At some point you should feel familiar with most of the material in
10.) Golub, G., and Van Loan, C. (1996). "Matrix Computations" Johns Hopkins.
It's good to know some information theory. The classic is:
11.) Cover, T. and Thomas, J. "Elements of Information Theory" Wiley.
Finally, if you want to start to learn some more abstract math, you might want to start to learn some functional analysis (if you haven't already). Functional analysis is essentially linear algebra in infinite dimensions, and it's necessary for kernel methods, for nonparametric Bayesian methods, and for various other topics. Here's a book that I find very readable:
12.) Kreyszig, E. (1989). "Introductory Functional Analysis with Applications" Wiley.