b) Become an academic in computer science with a focus on artificial intelligence.
c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles.
d) Become a specialized computer vision/natural language processing focused MLE.
e) Become a generalist software engineer who can whip out the above if needed.
In no way is e) the inferior option.
Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of.
There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project.
(Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.)
2. Study the above, properly.
To study the math, Elements of Statistical Learning/Algorithms by Goodfellow.
Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub.
3. Pursue your goal in a publicly verifiable manner.
The Elements of Statistical Learning is by Hastie et al, not by Goodfellow. Goodfellow wrote Deep Learning. They are both available for free on their websites.
Do you want to:
a) Become an academic in mathematics/statistics.
b) Become an academic in computer science with a focus on artificial intelligence.
c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles.
d) Become a specialized computer vision/natural language processing focused MLE.
e) Become a generalist software engineer who can whip out the above if needed.
In no way is e) the inferior option.
Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of.
There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project.
(Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.)
2. Study the above, properly.
To study the math, Elements of Statistical Learning/Algorithms by Goodfellow.
Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub.
3. Pursue your goal in a publicly verifiable manner.
See:
https://news.ycombinator.com/item?id=32071137