Soft & Hard Margin Support Vector Machine (SVM)| Machine Learning # 13
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📚About
This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning model associated with learning algorithms that analyze data used for classification and regression analysis. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997), it presents one of the most robust prediction methods, based on the statistical learning framework or VC theory proposed by Vapnik and Chervonenkis (1974) and Vapnik (1982, 1995).
⏲Outline⏲
00:00:00 Introduction
00:01:11 Support Vector Machines
00:03:55 Supporting Vectors and Hyperplanes
00:07:05 SVM Mathematical Modelling
00:08:58 Hard Margin SVM
00:47:21 Outlier Sensitivity & Linear Separability
00:49:11 Hard Margin SVM on Python
01:13:15 Soft Margin SVM
01:27:09 Soft Margin SVM on Python
01:31:47 Outro
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Lecture 1: Introduction • Introduction - Machine Learning # 1
Lecture 2: Binary Classification & SGD Classifier • Stochastic Gradient Descent Classifie...
Lecture 3: Performance Measures • Performance Measures - Machine Learni...
Lecture 4: Multiclass classification & Cross Validation • Multiclass classification & Cross Val...
Lecture 5: Gradient Descent • Gradient Descent - Machine Learning # 5
Lecture 6: Multilabel and Multioutput Classification • Multilabel and Multioutput Classifica...
Lecture 7: Linear Regression with Louis from "What is Artificial Intelligence" • Linear Regression | Machine Learning # 7
Lecture 8: Polynomial Regression feat. Luis Serrano & YouTube's Video Recommendation Algorithm • Polynomial Regression w Luis Serrano ...
Lecture 9: Simulated Annealing x SGD x Mini-batch • Simulated Annealing x SGD x Mini-batc...
Lecture 10: Ridge Regression • Ridge Regression | Tikhonov Regulariz...
Lecture 11: LASSO Regression and Elastic-Net Regression • LASSO Regression & Elastic-Net Regres...
Lecture 12: Logistic Regression & SoftMax Regression • Logistic Regression & SoftMax Regress...
SVM Convex Optimization Application: • Support Vector Machine (SVM) in Machi...
Quadratic Programming: • Lecture 6 | Quadratic Programs | Conv...
KKT conditions: • Lecture 18 | KKT Conditions | Convex ...
Complementary slackness: • Lecture 17 | Complementary Slackness ...
Lagrange Dual Function: • Lecture 14 | Lagrange Dual Function |...
Lagrange Dual Problem: • Lecture 15 | Lagrange Dual Problem | ...
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Instructor: Dr. Ahmad Bazzi
IG: / drahmadbazzi
Browser: https://www.google.com/chrome/
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Credits:
Google
https://www.google.com/
Google Photos
https://www.google.com/photos/about/
TensorFlow
https://www.tensorflow.org/
scikit-learn
https://scikit-learn.org/stable/
Numpy
https://numpy.org/
Microsoft OneNote
https://www.onenote.com/signin?wdorig...
Python
https://www.python.org/
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References:
[1] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.
https://www.amazon.com/Hands-Machine-...
[2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
https://www.amazon.com/Pattern-Recogn...
[3] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
https://www.amazon.com/Elements-Stati...
[4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019.
https://www.amazon.com/Hundred-Page-M...
[5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
https://www.amazon.com/Deep-Learning-...
[6] Chollet, Francois. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG, 2018.
https://www.amazon.com/Deep-Learning-...
[7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018.
https://www.amazon.com/Advances-Finan...
[8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012.
https://www.amazon.com/Pattern-Classi...
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