fellowship machine learning - dsi.unive.it
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Machine Learning Fellowship
Proprietary and confidential © 2016 Startup.ML..
Our Work
Trading Adversarial.AIStartups
How Do Machines Learn?
inputs modelpredictions
numberscategoriessparsetextpixels...
parametersinstances
yes/noclassnumbercluster...
What’s in the Model?Representation Evaluation Optimization
instances k-nearest neighbor support vector machinehyperplanes naïve bayes logistic regressiondecision treesset of rules propositional rules logic programsneural networksgraphical models bayesian networks conditional random fields
accuracy / error rateprecision and recallsquared errorlikelihoodposterior probabilityinformation gaink-l divergencecost/utilitymargin
combinatorial optimization greedy search beam search branch-and-boundcontinuous optimization unconstrained gradient descent conjugate gradient quasi-newton methodconstrained linear programming quadratic programming
What’s in the Model?Representation Evaluation Optimization
instances k-nearest neighbor support vector machinehyperplanes naïve bayes logistic regressiondecision treesset of rules propositional rules logic programsneural networksgraphical models bayesian networks conditional random fields
accuracy / error rateprecision and recallsquared errorlikelihoodposterior probabilityinformation gaink-l divergencecost/utilitymargin
combinatorial optimization greedy search beam search branch-and-boundcontinuous optimization unconstrained gradient descent conjugate gradient quasi-newton methodconstrained linear programming quadratic programming
What’s in the Model?Representation Evaluation Optimization
instances k-nearest neighbor support vector machinehyperplanes naïve bayes logistic regressiondecision treesset of rules propositional rules logic programsneural networksgraphical models bayesian networks conditional random fields
accuracy / error rateprecision and recallsquared errorlikelihoodposterior probabilityinformation gaink-l divergencecost/utilitymargin
combinatorial optimization greedy search beam search branch-and-boundcontinuous optimization unconstrained gradient descent conjugate gradient quasi-newton methodconstrained linear programming quadratic programming
What’s in the Model?Representation Evaluation Optimization
instances k-nearest neighbor support vector machinehyperplanes naïve bayes logistic regressiondecision treesset of rules propositional rules logic programsneural networksgraphical models bayesian networks conditional random fields
accuracy / error rateprecision and recallsquared errorlikelihoodposterior probabilityinformation gaink-l divergencecost/utilitymargin
combinatorial optimization greedy search beam search branch-and-boundcontinuous optimization unconstrained gradient descent conjugate gradient quasi-newton methodconstrained linear programming quadratic programming
periodic table of machine learning libraries 300+
All Models of Learning Have Flaws
Bayesian Graphical Decision Trees GANS
Kernel Machines BoostingNeural Networks Stacking
Meta Learning Online Reductions Reinforcement
http://hunch.net/?p=224
Proprietary and confidential © 2016 Startup.ML..
Machine Learning ChallengesLack of labeled data
Model decay / indeterminate retraining intervals
Extreme class imbalance
Dealing with adversarial examples (remember Tay?)
Counterfactual conditions
Feedback loops from supervision
data science is an extremely powerful art practiced by an extremely small community of artists
Data Science Skills
ML PhDs Statisticians / Mathematicians
Computer Scientists
Scientists Business Analysts / PMs
ML Theory & Math
Bayesian Stats
Coding
Optimization Theory
Distributed Systems
Visualization
Soft Skills
Quant + Software Engineer Pair
ML PhDs Statisticians / Mathematicians
Computer Scientists
Scientists Business Analysts / PMs
ML Theory & Math
Bayesian Stats
Coding
Optimization Theory
Distributed Systems
Visualization
Soft Skills
Software Engineer + Scientist Pair
ML PhDs Statisticians / Mathematicians
Computer Scientists
Scientists Business Analysts / PMs
ML Theory & Math
Bayesian Stats
Coding
Optimization Theory
Distributed Systems
Visualization
Soft Skills
ML PhD + PM Pair
ML PhDs Statisticians / Mathematicians
Computer Scientists
Scientists Business Analysts / PMs
ML Theory & Math
Bayesian Stats
Coding
Optimization Theory
Distributed Systems
Visualization
Soft Skills
Startup.ML Fellowship
Training Qualified Practitioners
Real startup and industry projects
Immersive 4 month program
Agile software development methodology
Mentoring by experienced data scientists
Discussion with an active practitioner every Friday
Proprietary and confidential © 2016 Startup.ML..
AI for Adversarial Environments
network intrusion, data breach, security monitoring, counterfeiting, arbitrage, phishing, social engineering, internal fraud ...
Proprietary and confidential © 2016 Startup.ML..
Pentesting ProcessScan the network
Port scan all hosts
Perform OS detection
Launch matching exploit
Proprietary and confidential © 2016 Startup.ML..
Honeypot
Proprietary and confidential © 2016 Startup.ML..
Reinforcement Learning
Agent Environment
Action
Observation, Reward
Synthetic Hacker
Thank [email protected]