Overview Science Data Acquisition Math Machines Conclusion Math Science and Machines ... or what I wish I’d known when I was younger Jaroslav Vážný Masaryk University / Astronomical Institute / Gauss Algorithmic / 4comfort.cz 5. prosince 2013 Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Concepts introduced in this talk What is Science? Science Reproducibility Paradigm shift? Probability Math Examples MOOC = new era? Education Computer literacy? Math Science HW/SW analogy and Machines Supervised Machine Learning Dimensionality reduction Machines Unspurevised Examples Data Mining What is Big Data anyway? What to study Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion What is Science? Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Why is important? ∇ × E = − ∂ B ∇ · E = 0 ∂ t , (1) ∇ × B = 1 ∂ E ∇ · B = 0 (2) ∂ t . c 2 Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Why is important? ∇ × E = − ∂ B ∇ · E = 0 ∂ t , (1) ∇ × B = 1 ∂ E ∇ · B = 0 (2) ∂ t . c 2 1 √ µ 0 ε 0 = 2 . 99792458 × 10 8 m s − 1 Miracle happen c = Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Reproducibility http://jakevdp.github.io/blog/2013/10/26/ big-data-brain-drain/ http://nbviewer.ipython.org/ http://pdos.csail.mit.edu/scigen/ ;-) Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Data Avalanche? Large Synoptic Survey Telescope 20 TB per night 60 PB for the raw data (after 10 years) 15 PB for the catalog database The total data volume after processing will be several hundred PB CERN 1 PB per day Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Sloan Digital Sky Survey Why is it important? Lots of data (>10 6 objects) Perfect documentation Tools to access the data Where I can learn it? http://www.sdss3.org/ Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Virtual Observatory Why is it important? Uniform access to astronomy data Based on Web standards Many tools with vo support (Topcat, Aladin, Tapsh) Where I can learn it? http://physics.muni.cz/~vazny/wiki/index.php/ Diploma_work Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Probability Test your intuition! Roll dice. 5 times you got 6. What is P(6)=? Monty Hall problem Show examples in IPython! 1 2 ? ? Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion MOOC == new era? https://www.khanacademy.org/ https://www.coursera.org/ https://www.udacity.com/ https://www.edx.org/ Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion What is Machine Learning (Data astrology) Data Mining Artificial Inteligence Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Supervised Machine Learning Supervised Learning Model Training Text, Feature Documents, Vectors Images, etc. Machine Learning Algorithm Labels Feature Vector New Text, Document, Expected Predictive Image, Model Label etc. Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Unsupervised Machine Learning Unsupervised Learning Model Training Text, Feature Documents, Vectors Images, etc. Machine Learning Algorithm Feature Vector New Text, Likelihood Document, Predictive or Cluster ID Image, Model or Better Representation etc. Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Example of feature extraction Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Example: Decison Tree ug <= 0.663668 1 | gr <= -0.191208: 1 (7.0) 2 | gr > -0.191208: 3 (104.0/5.0) 3 ug > 0.663668 4 | ri <= 0.285854: 1 (88.0/5.0) 5 | ri > 0.285854 6 | | ri <= 0.314657 7 | | | gr <= 0.692108: 2 (6.0) 8 | | | gr > 0.692108: 1 (3.0) 9 | | ri > 0.314657: 2 (90.0/2.0) 10 Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Example: Suport Vector Machine Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion References http://ipython.org/ http://www.greenteapress.com/thinkstats/ http://www.greenteapress.com/thinkpython/ http://scikit-learn.org/stable/ http://pandas.pydata.org/ http://jakevdp.github.io/ blog/2013/10/26/big-data-brain-drain/ http://www.galaxyzoo.org/ http://www.planethunters.org/ http://www.sdss3.org/ Jaroslav Vážný Practical approach
Overview Science Data Acquisition Math Machines Conclusion Discussion Jaroslav Vážný Practical approach
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