This document provides a list of topics related to data science, machine learning, social modeling, and decision making. Some of the key topics included are neural networks, random processes, linear and nonlinear regression models, emergence, growth models, heuristics, Markov processes, game theory, and prediction. The list touches on concepts from mathematics, computer science, economics, and other domains relevant to analyzing and modeling data.

1. Statistics
Computer
Science
Domain
Expertise
Data
Science
Machine
Learnin
g
Drew Conway
Data Science

3. Machine Learning: Neural Network

4. 4
Social models:
Segregation and peer
effects
Aggregation of numbers,
preferences, ?
Randomness, random
processes ,random walks
Cellular automata for social
and biological systems
Convergence and optimality
Probability
Social models:
Segregation and peer
effects
Decision making: rational
actor, behavioral, rule based
Numerical and categorical
data
Linear and nonlinear
Regression and
classification models
Emergence, tipping points,
contagion, SIS, diffusion,
percolation
Growth models, exponential
growth, Solow growth model
Problem solving,
perspectives and innovation
Heuristics
Problem solving and teams
Markov processes
Decision models
Decision models
Coordination and culture
Emergence of culture
Path dependence
Chaos
Increasing returns to scale
Network, structure, logic,
formation
Skills and luck
Game theory and Colonel
Blotto game
Decision models
Competition
Cooperation and collective
action
Prediction and diversity
The Many Model Thinker