Adaptive Data Analysis Machine learning in science and society - PowerPoint PPT Presentation

Adaptive Data Analysis Machine learning in science and society Christos Dimitrakakis August 21, 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 1 / 53

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 1 / 53

Introduction to machine learning 1 Introduction to machine learning Data analysis, learning and planning Experiment design Bayesian inference. Course overview 2 Nearest neighbours 3 Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 2 / 53

Introduction to machine learning Scientific applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 3 / 53

Introduction to machine learning Scientific applications Interpretability, Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 3 / 53

Introduction to machine learning Pervasive “intelligent” systems Web advertising Home assistants Lending Autonomous vehicles Public policy Ridesharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 4 / 53

Introduction to machine learning Pervasive “intelligent” systems Web advertising Privacy, Fairness, Safety Home assistants Lending Autonomous vehicles Public policy Ridesharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 4 / 53

Introduction to machine learning Data analysis, learning and planning What can machine learning do? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 5 / 53

Introduction to machine learning Data analysis, learning and planning Can machines learn from data? An unsupervised learning problem: topic modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 6 / 53

Introduction to machine learning Data analysis, learning and planning Can machines learn from data? A supervised learning problem: object recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 6 / 53

Introduction to machine learning Data analysis, learning and planning Can machines learn from their mistakes? Reinforcement learning Take actions a 1 , . . . , a t , so as to maximise utility U = ∑ T t =1 r t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 7 / 53

Introduction to machine learning Data analysis, learning and planning Can machines make complex plans? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 8 / 53

Introduction to machine learning Data analysis, learning and planning Machines can make complex plans! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 9 / 53

Introduction to machine learning Experiment design The scientific process as machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 10 / 53

Introduction to machine learning Experiment design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 11 / 53

Introduction to machine learning Experiment design Adam, the robot scientist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 12 / 53

Introduction to machine learning Experiment design Drug discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 13 / 53

Introduction to machine learning Experiment design Drawing conclusions from results experiment hypothesis conclusion result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 14 / 53

Introduction to machine learning Bayesian inference. Tycho Brahe’s minute eye measurements Figure: Tycho’s measurements of the orbit of Mars and the conclusion about the actual orbits, under the assumption of an earth-centric universe with circular orbits. Hypothesis: Earth-centric, Circular orbits . . . . . . . . . . . . . . . . . . . . Conclusion: Specific circular orbits . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 15 / 53

Introduction to machine learning Bayesian inference. Johannes Kepler’s alternative hypothesis Hypothesis: Circular or elliptic orbits Conclusion: Specific elliptic orbits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 16 / 53

Introduction to machine learning Bayesian inference. 200 years later, Gauss formalised this statistically . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 17 / 53

Introduction to machine learning Bayesian inference. A warning: The dead salmon mirage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 18 / 53

Introduction to machine learning Bayesian inference. A simple simulation study src/reproducibility/mri_analysis.ipynb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 19 / 53

Introduction to machine learning Bayesian inference. Planning future experiments experiment hypothesis conclusion result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 20 / 53

Introduction to machine learning Bayesian inference. Planning experiments is like Tic-Tac-Toe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 21 / 53

Introduction to machine learning Bayesian inference. Eve, another robot scientist Discovered a malaria drug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 22 / 53

Introduction to machine learning Course overview Machine learning in practice Avoiding pitfalls Choosing hypotheses. Correctly interpreting conclusions. Using a good testing methodology. Machine learning in society Privacy Fairness Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 23 / 53

Introduction to machine learning Course overview Machine learning in practice Avoiding pitfalls Choosing hypotheses. Correctly interpreting conclusions. Using a good testing methodology. Machine learning in society Privacy — Credit risk. Fairness Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 23 / 53

Introduction to machine learning Course overview Machine learning in practice Avoiding pitfalls Choosing hypotheses. Correctly interpreting conclusions. Using a good testing methodology. Machine learning in society Privacy — Credit risk. Fairness — Job market. Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Adaptive Data Analysis August 21, 2019 23 / 53