Automated machine learning (AutoML) aims to automate the machine learning process including feature engineering, model selection, and algorithm selection. The AutoML framework includes an optimizer to determine the search space and techniques for evaluation, an evaluator to measure the performance of learning tools, and a learning process. Key techniques for the optimizer include simple search spaces for evaluation and more complex derivative-free optimization. Techniques for the evaluator include direct evaluation on a validation set as well as early stopping and sub-sampling for faster evaluation.

1. Automated
Machine Learning

2. AutoMLAgenda
Machine Learning
Vs. Automation
Automated Machine
Learning AutoML framework
What to automate Feature Engineering
Model & Algorithm
Selection
Full Scope Techniques for optimizer
Techniques for
evaluator
ES LAB8 - Basics Of Scientific Research

3. Machine Learning (ML) Vs. Automation
Automation
ES LAB8 - Basics Of Scientific Research
Machine Learning (ML)
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4. ES LAB8 - Basics Of Scientific Research
Automated Machine Learning (AutoML)
Data Prediction
Controller
 Measure the performance of
learning tools that provided
by the optimizer.
 Training Data.
 Efficient (time and accurate).
 Better performance.
 Determine Search space
EvaluationOptimizer

5. optimizer
evaluator
Learning process
Features, models, algorithm
Learning Tool
Determine Search space
Measure performance
 Basie vs Proposed framework:
Experience technologies
Setup
Figure out configuration,
Determine search space
optimizer
evaluator
ES LAB8 - Basics Of Scientific Research
AutoML Framework

6. Learning process
Techniques of controller (Optimizer )
Feature
engineering
Model
selection
Algorithm
selection
Full scope
include three parts above
ES LAB8 - Basics Of Scientific Research
What to automate

7. Feature projection
Ex: multiparton two features.
Dimension reduction
Have Redundancy
Dimensionality is too high
Feature encoding.
Re-interprets feature.
Ex: transforming categorical
feature to continuous feature.
ES LAB8 - Basics Of Scientific Research
Feature Engineering
Image source: https://link.springer.com/article/10.1007/s00500-019-04628-6

8.  Goal of algorithm selection
Automatically find an optimization
algorithm so that efficiency and
performance can be balanced.
Efficiency focus on the choice of
optimization algorithm.
 Model Selection
 Process of finding the best model
for predicting.
ES LAB8 - Basics Of Scientific Research
Model & Algorithm Selection
Image source: https://www.geeksforgeeks.org/top-10-algorithms-every-machine-learning-engineer-should-know/

9.  Feature Selection
 Model Selection
 Algorithm Selection
ES LAB8 - Basics Of Scientific Research
Full Scope
Image source: https://github.com/DataSystemsGroupUT/SmartML

10. Goal: To update | generate configuration for learning tools.
1. Simple search space
Each configuration in the search space can be
evaluated independently.
Two common “Grid and random search”
 Advantage: Simplicity applied in AutoML.
Disadvantage: does not exploit knowledge
gained from the past evaluation.
ES LAB8 - Basics Of Scientific Research
Techniques for optimizer
Image source: https://pic2.zhimg.com/80/v2-0100837d61e7c7ee3776bf706596c145_720w.jpg

11.  Derivative-Free Optimization
oExploit Knowledge Gained From The Past
Evaluation.
oMore complex but good performance.
Gradient descent
Optimization is very complex
Common case in the AutoML.
Gradient descent is the most efficient.
ES LAB8 - Basics Of Scientific Research
Techniques for optimizer
Image source: https://mragungsetiaji.github.io/python/machine%20learning/2018/09/16/gradient-descent.html

12. Main aim : Measures The Performance
 Direct evaluation :
 The most common applied in AutoML
 Measure on validation set.
Advantages Problems
 Simple and accurate  Very time-consuming.
ES LAB8 - Basics Of Scientific Research
Techniques for evaluator
Image source: https://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-14-evaluation-and-credibility

13.  Other techniques
 Sub-sampling
Faster evaluation using subset sampling and features.
 Early stop
Avoid overfitting but can cause a general error.
ES LAB8 - Basics Of Scientific Research
Techniques for evaluator
Image source: https://www.youtube.com/watch?v=I-JKxcpbRT4&fbclid=IwAR1tc4xwFr5ucaCH-qyvOL9kglJ255V-K9ORHatm9kM9BEkQ7Pldr6gIkNg&ab_channel=HugoLarochelle

14.  Early Stop
ES LAB8 - Basics Of Scientific Research
Techniques for evaluator
Image source: https://keeeto.github.io/blog/bias_variance/?fbclid=IwAR1KWabxWabeQ1Ff1HPzxb4khgj3MJ4MWyoWbY0w1YXwoM_Ux-SimAci0yA#:~:text=If%20we%20have%20overfitted%2C%20this,build%20an%20overly%20complex%20model.&text=An%20example%20of%20overfitting.,fit%20the%20true%20function%20correctly

15. Solution?
Techniques for evaluator
 A combination of the last
techniques for optimizer
ES LAB8 - Basics Of Scientific Research
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16. “The greatest benefit of
machine learning may ultimately
be not what the machines learn but
what we learn by teaching them.”
Pedro Domingos.
ES LAB8 - Basics Of Scientific Research
Image source: https://pamdidner.com/wp-content/uploads/2018/08/Picture1.png

17. Team
Members
Mohamed Ibrahim
Doaa Abdelhadi
Sarah AliMoamen Sobhy
Aya Ghorabah
Ashraf Mohamed

18. ES LAB8 - Basics Of Scientific Research
October 2020
Thanks