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

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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
  • 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
  • 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.
  • 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