Machine Learning refers to a set of tools for modeling and understanding complex datasets.
With the explosion of “Big Data” problems, machine learning has become a very hot field in many scientific areas as well as bioinformatics, cancer research, and other biology disciplines. People with statistical learning skills are in high demand.
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Introduction to Applied Machine Learning
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Biological Sciences faculty
Biophysics Department
Introduction to Applied Machine Learning
Presented By
Alireza Doustmohammadi
Graduate Student in Bioinformatics
January 2021
5. Why do we need to prediction?
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Welcome To de Era of
Big Data …..
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[https://www.ncbi.nlm.nih.gov/genbank/statistics/]
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[https://www.rcsb.org/stats/summary]
Molecular Type X-ray NMR EM Multiple methods Neutron Other Total
Protein (only) 135896 34576 4544 165 67 34 152280
Protein/NA 7177 269 1603 3 0 0 9052
Nucleic acid (only) 2158 1340 53 7 2 1 3561
Other 149 31 3 0 0 0 183
Total 153400 13453 6814 181 69 37 173754
PDB Data Distribution by Experimental Method and Molecular Type:
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Basic Concepts &
Nomenclatures
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28. Algorithm (Model Selection): over fitting
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“All models are wrong,
but some are useful.”
George Box, British Statistician
1919-213
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Increasing the size of the data set may reduce the over-fitting
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Bias – variance Trade off
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Increase Flexibility:
▪ Bias tends to initially decrease
faster than variance increases
▪ At some point has little impact on
the bias but starts to significantly
increase the variance.