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Introduction to Applied Machine Learning
- 1. 1 of 34 ML - 2021 Biological Sciences faculty Biophysics Department Introduction to Applied Machine Learning Presented By Alireza Doustmohammadi Graduate Student in Bioinformatics January 2021
- 2. Contents Introduction Why do we need to prediction? Central Dogma of Prediction 2 of 34 ML - 2021
- 3. Reference 3 of 34 ML - 2021
- 4. Outline 4 of 34 Supervised Learning Unsupervised Learning Ensemble Learning ML - 2021
- 5. Why do we need to prediction? 5 of 34 ML - 2021
- 6. Why do we need to prediction? 6 of 34 Welcome To de Era of Big Data ….. ML - 2021
- 7. Why do we need to prediction? 7 of 34 ML - 2021
- 8. Why do we need to prediction? 8 of 34 [https://www.ncbi.nlm.nih.gov/genbank/statistics/] ML - 2021
- 9. Why do we need to prediction? 9 of 34 [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: ML - 2021
- 10. Why do we need to prediction? 10 of 34 Basic Concepts & Nomenclatures ML - 2021
- 11. Central Dogma of Prediction 11 of 34 ML - 2021
- 12. Central Dogma of Prediction 12 of 34 ML - 2021
- 13. Central Dogma of Prediction 13 of 34 • Defining the Questions • Data Collection • Feature Extraction • Preprocessing & Feature Selection • Algorithm (Classifier) • Evaluation • Redesign the Algorithm (Parameter Tuning) ML - 2021
- 14. Features 14 of 34 ✓ Good representation of data ✓ Data Compression ✓ Need to expert’s knowledge ML - 2021
- 15. Data Matrix 15 of 34 ML - 2021
- 16. X , Y Relation 16 of 34 ML - 2021
- 17. X , Y Relation 17 of 34 ML - 2021
- 18. X , Y Relation 18 of 34 ML - 2021
- 19. X , Y Relation 19 of 34 ML - 2021
- 20. Reducible and irreducible error 20 of 34 Reducible Irreducible Goal: Minimizing the reducible error ML - 2021
- 21. 21 of 34 Preprocessing & Feature Selection ML - 2021
- 22. Preprocessing & Feature Selection 22 of 34 ML - 2021
- 23. Preprocessing & Feature Selection 23 of 34 ML - 2021 Data Challenges: ➢ Miss Value ➢ Low-frequency variant Features ➢ Outliers
- 24. Preprocessing & Feature Selection 24 of 34 ML - 2021 Blessing and Curse of Dimensionality
- 25. 25 of 34 Algorithms ML - 2021
- 26. Most Common Algorithms 26 of 34 KNN Regression MLP ANN SVM Decision Tree Random Forest Bayesian Net ML - 2021
- 27. Central Dogma of Prediction 27 of 34 ML - 2021
- 28. Algorithm (Model Selection): over fitting 28 of 34 ML - 2021 “All models are wrong, but some are useful.” George Box, British Statistician 1919-213
- 29. Algorithm (Model Selection): over fitting 29 of 34 ML - 2021
- 30. Algorithm (Model Selection): over fitting 30 of 34 Increasing the size of the data set may reduce the over-fitting ML - 2021
- 31. Algorithm (Model Selection): over fitting 31 of 34 Bias – variance Trade off ML - 2021 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.
- 32. The Bias-Variance Trade-off 32 of 34 ML - 2021
- 33. In Sample Error Vs on Sample error 33 of 34 ML - 2021 • Train data • Bias In sample error • Test data • Variance Out sample error
- 34. The peaking paradox 34 of 34 ML - 2021