Given the modern state of machine learning technologies and tools (e.g. TensorFlow, Theano, etc.), it seems like entry threshold have recently lowered and it is enough to be able to program on, say, Python, to build interesting things. Another source that supports this point is Machine Learning Specialization on Coursera, that states the following in their FAQ:
What background knowledge is necessary?
You should have some experience with computer programming; most assignments in this Specialization will use the Python programming language. This Specialization is designed specifically for scientists and software developers who want to expand their skills into data science and machine learning, but is appropriate for anyone with basic math and programming skills and an interest in deriving intelligence from data.
On the other hand, there are plenty of other on-line courses (e.g. Stanford Machine Learning on Coursera or Google's Deep Learning on Udacity), as well as classical books, like S. Haykin, Neural Networks: A Comprehensive Foundation, packed with mathematics. Even though I was studying math for several years in university, including statistics, matrices, integral calculus and so on, it's been so long unused that I feel despair by merely looking at those equations. Even Concrete Mathematics by Knuth is discerned at such slow pace that it is seems impossible to finish it at all.
Thus, the following questions arise:
- Is it reasonable for someone with shallow knowledge in math but programming skills to dive into neural network/machine learning studies?
- Is it possible to build interesting projects in this area, like those playing atari, using only high-level tools?
Or would it take so much time, that it is better not to make oneself struggle and do something else?
Best Answer
It depends upon your type of Work:
Maths is required if you are working in an applied Science role, i.e. you try to experiments with the known things in Hand i.e. try word embeddings, may be with CNN, and see if the results are good or NOT.
On the other hand , a lot of maths is required if you want to end up as a research scientist, example finding new ways to represent word embedding, or improve the existing word embeddings itself, in case of Text mining.
On the other hand, If you are working as a Software engineer in Machine Learning OR a Machine Learning Engineer, then you just need to train the models using existing knowledge of doing things and Tune it for better performance.
There is a trade off between research and Engineering. More towards Research is More Maths, but More towards Engineering is lesser Maths and More on performance of system in Production.
Another Example to explain would be, for chat Bots. Research Scientist with Maths background require to write a paper for a new models like how LSTM works and can be Used. A applied scientiest will try out a business problem like building a chat bot with LSTM first and publish papers how it worked for them in Labs. A Machine Learning Engineer will replicate the concept which the Applied Scientist had published for their engineering Work (i.e. need to understand maths of paper and replicate it in code, that's it.)
Hope this helps with respect to requirement of knowledge of Maths in Machine Learning