There is a classic post here https://news.ycombinator.com/item?id=10954508:
" The Geneticists: Use evolutionary principles to have a model organize itself The Bayesians: Pick good priors and use Bayesian statistics
The Symbolists: Use top-down approaches to modeling cognition, using symbols and hand-crafted features
The Conspirators: Hinton, Lecun, Bengio et al. End-to-end deep learning without manual feature engineering
The Swiss School: Schmidhuber et al. LSTM's as a path to general AI.
The Russians: Use Support Vector Machines and its strong theoretical foundation
The Competitors: Only care about performance and generalization robustness. Not shy to build extremely slow and complex models.
The Speed Freaks: Care about fast convergence, simplicity, online learning, ease of use, scalability.
The Tree Huggers: Use mostly tree-based models, like Random Forests and Gradient Boosted Decision Trees
The Compressors: View cognition as compression. Compressed sensing, approximate matrix factorization
The Kitchen-sinkers: View learning as brute-force computation. Throw lots of feature transforms and random models and kernels at a problem
The Reinforcement learners: Look for feedback loops to add to the problem definition. The environment of the model is important.
The Complexities: Use methods and approaches from physics, dynamical systems and complexity/information theory.
The Theorists: Will not use a method, if there is no clear theory to explain it
The Pragmatists: Will use an effective method, to show that there needs to be a theory to explain it
The Cognitive Scientists: Build machine learning models to better understand (human) cognition
The Doom-sayers: ML Practitioners who worry about the singularity and care about beating human performance
The Socialists: View machine learning as a possible danger to society. Study algorithmic bias.
The Engineers: Worry about implementation, pipe-line jungles, drift, data quality.
The Combiners: Try to use the strengths of different approaches, while eliminating their weaknesses.
The Pac Learners: Search for the best hypothesis that is both accurate and computationally tractable. "
There is a classic post here https://news.ycombinator.com/item?id=10954508:
" The Geneticists: Use evolutionary principles to have a model organize itself The Bayesians: Pick good priors and use Bayesian statistics
The Symbolists: Use top-down approaches to modeling cognition, using symbols and hand-crafted features
The Conspirators: Hinton, Lecun, Bengio et al. End-to-end deep learning without manual feature engineering
The Swiss School: Schmidhuber et al. LSTM's as a path to general AI.
The Russians: Use Support Vector Machines and its strong theoretical foundation
The Competitors: Only care about performance and generalization robustness. Not shy to build extremely slow and complex models.
The Speed Freaks: Care about fast convergence, simplicity, online learning, ease of use, scalability.
The Tree Huggers: Use mostly tree-based models, like Random Forests and Gradient Boosted Decision Trees
The Compressors: View cognition as compression. Compressed sensing, approximate matrix factorization
The Kitchen-sinkers: View learning as brute-force computation. Throw lots of feature transforms and random models and kernels at a problem
The Reinforcement learners: Look for feedback loops to add to the problem definition. The environment of the model is important.
The Complexities: Use methods and approaches from physics, dynamical systems and complexity/information theory.
The Theorists: Will not use a method, if there is no clear theory to explain it
The Pragmatists: Will use an effective method, to show that there needs to be a theory to explain it
The Cognitive Scientists: Build machine learning models to better understand (human) cognition
The Doom-sayers: ML Practitioners who worry about the singularity and care about beating human performance
The Socialists: View machine learning as a possible danger to society. Study algorithmic bias.
The Engineers: Worry about implementation, pipe-line jungles, drift, data quality.
The Combiners: Try to use the strengths of different approaches, while eliminating their weaknesses.
The Pac Learners: Search for the best hypothesis that is both accurate and computationally tractable. "