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Machine Learning: Lecture 27: Neural networks (continued)
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Classroom Contents
Machine Learning - Spring 2023
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- 1 Machine Learning: Lecture 1: Introduction
- 2 Machine Learning: Lecture 1: Course information
- 3 Machine Learning: Lecture 2: Supervised Learning - The setup
- 4 Machine Learning: Lecture 3a: Supervised Learning - The Setup (continued)
- 5 Machine Learning: Lecture 3b: Decision Trees
- 6 Machine Learning: Lecture 4: Decision trees (continued)
- 7 Machine learning: Lecture 5a: Decision trees and overfitting (continued)
- 8 Machine learning: Lecture 5b: Linear Models
- 9 Machine Learning: Lecture 6a: Linear models (continued)
- 10 Machine Learning: Lecture 6b: Online learning
- 11 Machine Learning: Lecture 7: Online Learning (continued)
- 12 Machine Learning: Lecture 8a: Online Learning (continued)
- 13 Machine Learning: Lecture 8b: Perceptron
- 14 Machine Learning: Lecture 9: Perceptron (continued)
- 15 Machine Learning: Lecture 10: Perceptron (continued)
- 16 Machine Learining: Lecture 11: Least Mean Square Regression
- 17 Machine Learning: Lecture 12a: Least mean square regression (continued)
- 18 Machine Learning: Lecture 12b: Computational Learning Theory
- 19 Machine Learning: Lecture 13: PAC learning
- 20 Machine Learning: Lecture 14: Occam's razor (continued)
- 21 Machine Learning: Lecture 15: Mid-semester review
- 22 Machine Learning: Lecture 16a: Learnability results
- 23 Machine Learning: Lecture 16b: Agnostic learning
- 24 Machine Learning: Lecture 17a: Agnostic learning (continued)
- 25 Machine Learning: Lecture 17b: Shattering and VC dimension
- 26 Machine Learning: Lecture 18a: VC dimensions (continued)
- 27 Machine Learning: Lecture 18b: Boosting and Ensembles
- 28 Machine Learning: Lecture 19: Boosting and ensembles (continued)
- 29 Machine Learning: Lecture 20: Support Vector Machines
- 30 Machine Learning: Lecture 21: Support Vector Machines (continued)
- 31 Machine Learning: Lecture 22: Stochastic Gradient Descent for SVM
- 32 Machine Learning: Lecture 23a: Learning as loss minimization
- 33 Machine learning: Lecture 23b: Bayesian learning
- 34 Machine Learning: Lecture 24a: Bayesian learning (continued)
- 35 Machine Learning: Lecture 24b: Discriminative and Generative models
- 36 Machine Learning: Lecture 25: Logistic regression
- 37 Machine Learning: Lecture 25: Introduction to neural networks
- 38 Machine Learning: Lecture 27: Neural networks (continued)
- 39 Machine Learning: Lecture 28: Practical concerns
