Comment by dagw | Hacker News Reader

Comment by dagw | Hacker News Reader

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ML and 'AI' are very multifaceted fields that require a strong foundation in mathematics.

As a mathematician with a strong foundation in all those things you mention (and more) I don't think it's really necessary. I've never found my knowledge of algebra tensors in any way useful or relevant when working with tensorflow for example. On rare occasions I might get some insight like that working with the Fourier transform of the data source might be easier than working directly with the data source, but even then all that really requires is knowing what a Fourier transform is/does and not so much about theory and analysis behind it.

A large part of ML today is very much an applied practical field. Collecting and cleaning data, selecting and normalizing features, understanding the pros and cons of the available algorithms for the problem at hand, knowing how to tune parameters, understanding the practical computational limitations of working with data that doesn't fit in RAM and so on. These are the skills most ML practitioners need.

If someone is interested and wants to contribute new knowledge to the field then they'll probably need to learn the math, but for solving most types of ML related problems that most companies have I've never needed any math taught after my first year at university. If you really understand everything taught in your first couple of linear algebra courses and in your intro statistics course you'll do fine.

> A large part of ML today is very much an applied practical field.

Agreed. Once you understand the difference between bias, variance, training, test and development sets, cross-validation, feature selection, normalization, precision, recall, F-score, Matthews correlation coefficient, regularization, imputation techniques for missing values, overfitting, etc. I.e. you know how to build and test models in a rigorous fashion, you're 90% of the way there. Knowing what these terms mean, and why you need to understand them is waaaay more important than understanding the math behind SVM. It almost becomes boring at that point, because it's the same crap over and over. Doing actual AI research, that's something completely different.

I mean just look at how very simple these Keras examples are, and these are really quite advanced and powerful deep learning models: https://github.com/keras-team/keras/tree/master/examples. You definitely do _not_ need a PhD or even a Masters, to understand, re-implement or tweak on any of these models if you understand how to rigorously test the resulting model.

Research: https://www.researchgate.net/publication/13853244_Long_Short...

Practice:

  model = Sequential()
  model.add(Embedding(max_features, 128, input_length=maxlen))
  model.add(Bidirectional(LSTM(64)))
  model.add(Dropout(0.5))
  model.add(Dense(1, activation='sigmoid'))

I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience

To answer that question and create a plan to tackle that problem requires knowledge and experience

Sure, but you don't necessarily need knowledge about tensor fields and Fourier analysis.

What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You need a Computer Science degree to develop and debug software". The libraries, frameworks and operationalising of ML models is mature enough at this point that it's accessible to a wider audience than just people with research-level academic backgrounds.

I'm not saying that your average web developer with no formal training can or even should be putting this kind of stuff in production. But someone with an undergraduate degree in Computer Science that's had a year or two of calculus and linear algebra and first year mathematical statistics should have no problem whatsoever in practice doing ML/AI. I mean look at this:

  from sklearn.model_selection import KFold
  # Define 10 fold cross-validation
  cv = KFold(n_splits=10)
  from sklearn.model_selection import GridSearchCV
  svm_model = GridSearchCV(svc_pipeline, param_grid=svc_parameters, scoring='f1_micro', cv=cv)
  svm_model.fit(X_train, y_train)

A few lines and you're doing hyperparameter optimization on an SVM model with cross-validation. What a time to be alive.

> I don't think it's really necessary. I've never found my knowledge of algebra tensors in any way useful or relevant when working with tensorflow for example.

If you're working towards such a specialised degree, the target shouldn't be "I can use Tensorflow", it should be "I can write a simpler version of Tensorflow".

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