Suggest edit — 15-03-19 Ideas for Learning ML/AI

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title: "15-03-19 Ideas for Learning ML/AI"
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# 15-03-19 Ideas for Learning ML/AI

Category: [[idea-lists-upon-request|Idea Lists (Upon Request)]]

[Read the original document](https://docs.google.com/document/d/1JRhjtXJyhO9tXfjcEVRFE7ZMGD2UFHqPb8VRsPXAyFs/edit?usp=drivesdk&sa=D&ust=1596495076939000&usg=AOvVaw3VEJzqyC4vmnxl4LVvtCx_)

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Ideas for Learning ML

So I’m already doing a number of things - data ventures, 181, 124. These are okay ways to learn, but I’m certain that there’s a lot that is hidden from me and a lot of motivation that I’m missing out on because my resources aren’t sufficiently exciting or efficient.

1. Kaggle Inclass. Go extremely hard on 181’s inclass competition. Do a postmortem on each competition, look up and execute the methods used by other teams. Lean on 181 TFs for places to find more information. 
2. Kaggle. Through data ventures and friend groups, do both Neural Network Kaggle competitions in the next few weeks. Set up an infrastructure and start optimizing within them. Get Ryan Adams to advise these games.
3. Apply ML to Basketball prediction market.
4. Preterm planning prediction with Jim Waldo and Sam Rosen. 
5. Search for research opportunities in ML on campus - ask Ryan and Parkes who to ask.
6. Applied predictive modeling seems like a good choice, as does metaacademy.
7. Draw up my own curriculum with resources and learn through that.
8. Go through all of the problems in Bishop in chapters that came before. 
9. Start a ML blog and detail all of my kaggle adventures as well as things that I’ve learned from homeworks and other projects involving ML.
10. Go hard using ML on datasets from companies that I’m interviewing with. 
11. Talk to my friends about projects that they’d like done that can use ML.
12. Predict events in the real world - grammys/social stuff, sports, etc.’
13. Do a literature review. Know the major texts and applied manuals, and evaluate them. consume the best resources.
14. Read papers! Find the most influential papers in the field, most cited, and read those.
15. Survey ML industry space! Generate a list of the most important organizations and what techniques they use. 
16. Understand Artificial Intelligence, perhaps by outlining AI: A Modern Approach
17. Read Kevin Murphy’s Textbook
18. Environment!!! Make friends who care about ML/AI and cement the ones who are interested. Get much closer to Aaron, Lili, Marco, David, and Brandon, and talk more about ML/AI with Andrew and Vincent.
19. List all professors whose work intersects with AI and ML and have conversations with them about how to get better at this toolset.
20. Hackerrank ML problems
21. Build my own high-level understanding of the field by outlining ML through wikipedia
22.

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*Source: [Original Google Doc](https://docs.google.com/document/d/1JRhjtXJyhO9tXfjcEVRFE7ZMGD2UFHqPb8VRsPXAyFs/edit?usp=drivesdk&sa=D&ust=1596495076939000&usg=AOvVaw3VEJzqyC4vmnxl4LVvtCx_)*