machine learning lecture

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1. 1▶Optimization in Machine Learning (Lecture 11): ApplicationsOptimization in Machine Learning Online ClassNotes
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2. 2▶Optimization in Machine Learning (Lecture 10): Difference of Submodular Opt, SCSC & SCSKOptimization in Machine Learning Online ClassNotes
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3. 3▶Optimization in Machine Learning (Lecture 9):Submodular Maximization and GreedyOptimization in Machine Learning Online ClassNotes
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4. 4▶Optimization in Machine Learning (Lecture 8): Polyhedra, Extensions, and Submodular MinimizationOptimization in Machine Learning Online ClassNotes
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5. 5▶Optimization in Machine Learning (Lecture 7 Continued): Submodular Information MeasuresOptimization in Machine Learning Online ClassNotes
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6. 6▶Optimization in Machine Learning: Lec 7 (Submodular Functions: Definitions, Examples, Properties)Optimization in Machine Learning Online ClassNotes
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7. 7▶Optimization in Machine Learning: Lecture 6.2 (SGD for Deep Learning)Optimization in Machine Learning Online ClassNotes
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8. 8▶Optimization in Machine Learning: Lecture 6.1 (Stochastic Gradient Descent)Optimization in Machine Learning Online ClassNotes
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9. 9▶Optimization in Machine Learning: Lecture 5 (Second Order Methods and Coordinate Descent)Optimization in Machine Learning Online ClassNotes
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10. 10▶Optimization in Machine Learning: Lec 4 (Conditional GD and Projected GD, Quasi-Newton, BFGS etc.)Optimization in Machine Learning Online ClassNotes
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11. 11▶Optimization in Machine Learning: Lec 3 (Gradient Descent Cont., Nesterov's GD, Proximal GD, Demos)Optimization in Machine Learning Online ClassNotes
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12. 12▶Optimization in Machine Learning: Lecture 2 (Convex Functions Cont, Analysis of Gradient Descent)Optimization in Machine Learning Online ClassNotes
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13. 13▶Optimization in Machine Learning: Lecture 1 (Outline, Logistics, Convexity)Optimization in Machine Learning Online ClassNotes
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14. 14▶Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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15. 15▶Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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16. 16▶Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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17. 17▶Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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18. 18▶Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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19. 19▶Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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20. 20▶Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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21. 21▶Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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22. 22▶Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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23. 23▶Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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24. 24▶Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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25. 25▶Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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26. 26▶Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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27. 27▶Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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28. 28▶Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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