Old maths topics
Python data analysis
R time series
Fundamentals of physics
Classification of phenomena by how easy they are to predict.
Properties of industry-related derivatives.
Automation - is it happening?
Economic analysis on admin data e.g. VAT data to estimate lifespan of firms.
test whether machine learning can 'discover' hidden structure (that is planted)
define usefulness & closeness to decision makers.
systematic way to uncover secrets knowledge about how things actually work.
wage data derivatives on sweden/norway wage data
lateral inhibition vs social norms.... implications for raising children
create notes on technologies across different books
booksonnews - multi-disciplinary analysis of news.
sig|nal - code to mark mathematics exams.
ONS outlier/trend detection - python code.
Dissertation - machine learning applied to macroeconomic/micro data.
Quantecon - translate Python code into R.
OBR - translate winsolve OBR code into Python.
Linear algebra udemy course.
book review web scraper classified by reviewer
Classifying prediction problems
r package ons statistics + build in methodology + analysis
not big data but small data, outlier/anomaly/weirdness detection
youtube video scanning of questions with elon - categorising the questions
model different models competing against each other in fake assets markets and see who wins/ get most returns
case studies of where data science has worked
reinforcement learning in a game with changing rules. i.e. learning how to learn
rather than hyperlinks like google or articles like wikipedia, build sorta mind maps for topics, which can then be trained upon to generate abstractions. it would be interseting if you had umm sorta particles interacting with each other if they would generate the same words for things as we swould so if particles moved aroudn and ate food and talked and coordinated, if those particles would generate abstractions for communication in the same way.
unsupervised learning based database, where store elements and then it looks for relationships between elements.
software to help management of staff/project management thinking about incentives etc. thinking about stakeholders
rather than trying to improve the learnign algorithms, what about trying to improve the quality of the data? obvious example is if trying to differentiate between a cat and a dog spend a lot of time looking at examples that are close to the boundary, i.e. where there is a higher probaiblity of misclassifying. but you can take it further and ask how to learn higher levels of abstraction, can you learn on the abstract data. so machine learning models always have the raw data and have to learn from scratch, but humans learn history etc. reading about the abstractions direclty, i guess this is transfer learning, but that is a function of the model, rather than the data? maybe learning is transfering hte model not the data that hte model was run on?