Self Study roadmap for Machine Learning

In summary, a PhD student in mathematics is considering working in Machine Learning and is looking for advice on resources and learning paths. There are several blogs and courses that he can follow to learn more about the subject.
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Hello, I am a second year PhD student in mathematics currently studying logic. I have recently begun questioning staying in the field, and I really think I may want to work in Machine Learning, at the very least in the theory of it. I am willing to self study from machine learning books, but quite frankly, I don’t know which ones to read, which are good, etc. Can anyone provide a nice roadmap for someone to learn machine learning (and neural networks) with my background? I know a tad bit of programming in Python. If there are any additional math subjects needed for background as well, I can study that too if necessary. Thanks in advance.
 
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  • #2
There are several blogs that you can follow to learn what's hot, what's not, and how to get into the fray. Be aware that there is a lot of hype going on right now and a lot of folks joining the party.

3blue1brown YouTube has a sequence of videos on Neural Nets, Linear Algebra and Calculus that are quite cool.

http://www.3blue1brown.com/

and there's a ton of courses but your best bet is to find someone to recommend one to you:

https://www.google.com/search?q=lea....chrome-ntp-vasco..0.1.15.4...103.ndQBquhc84E

Quantitative Economics has a course in using either Python or Julia for machine learning from a QE perspective:

https://lectures.quantecon.org/jl/index.html

I'm currently playing with Julia as I feel it will take the ML field by storm as it matures. Juli Computing has JuliaPro and JuliaBox products that provide IDE experiences in the language. JuliaPro is akin to Eclipse or Netbeans IDE and JuliaBox is Jupyter Notebooks IDE (which is really cool and great for learning). Both are free at some level.

www.juliacomputing.com
 
  • #3
Data Science is HOT right now!

Got me learning Python and ish.
 
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If you want to get some practical skills in this field I recommend you the Kaggle plataform, they have a section for learning : www.kaggle.com/learn/overview
There you can take part of competitions and get real skills (and even money) in machine learning.
 
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  • #5
Machine learning as a branch of statistics
https://www.amazon.com/dp/0387310738/?tag=pfamazon01-20
Pattern Recognition and Machine Learning by Christopher Bishop
https://www.amazon.com/dp/0262018020/?tag=pfamazon01-20
Machine Learning by Kevin Murphy
https://www.deeplearningbook.org/
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Machine learning as a branch of control theory
https://www.crcpress.com/Reinforcem...abuska-De-Schutter-Ernst/p/book/9781439821084
Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu, Robert Babuska, Bart De Schutter, and Damien Ernst
http://incompleteideas.net/book/the-book-2nd.html
Reinforcement Learning by Richard Sutton and Andrew Barto
 

FAQ: Self Study roadmap for Machine Learning

1. What is a self-study roadmap for Machine Learning?

A self-study roadmap for Machine Learning is a personalized learning plan that outlines the necessary steps and resources to become proficient in Machine Learning. It typically includes a combination of theoretical concepts, programming skills, and hands-on projects.

2. What are the benefits of following a self-study roadmap for Machine Learning?

Following a self-study roadmap for Machine Learning allows individuals to learn at their own pace and tailor their learning to their specific interests and goals. It also provides a structured approach to learning, ensuring that all necessary topics and skills are covered in a logical sequence.

3. What are the essential components of a self-study roadmap for Machine Learning?

A self-study roadmap for Machine Learning typically includes learning objectives, recommended resources (books, online courses, tutorials), programming languages and tools, and hands-on projects. It may also include a timeline or schedule for completing each component and tracking progress.

4. How long does it take to complete a self-study roadmap for Machine Learning?

The time it takes to complete a self-study roadmap for Machine Learning depends on the individual's prior knowledge, learning pace, and dedication. It can range from a few months to a few years. It is essential to focus on understanding the concepts rather than completing it within a specific time frame.

5. Are there any recommended resources for a self-study roadmap for Machine Learning?

Yes, there are various resources available for a self-study roadmap for Machine Learning, including online courses, books, tutorials, and open-source projects. Some popular resources include Coursera, Udemy, Kaggle, and GitHub. It is essential to research and choose resources that align with your learning style and goals.

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