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Machine Learning: Lecture 9a: On representations and learning
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Classroom Contents
Machine Learning - Spring 2024
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- 1 Machine Learning: Lecture 1: Introduction & Course Information
- 2 Machine Learning: Lecture 2: Supervised Learning: The setup
- 3 Machine Learning: Lecture 3a: Supervised Learning - The Setup (continued)
- 4 Machine Learning - Lecture 3b: Decision Trees
- 5 Machine Learning: Lecture 4: Learning decision trees
- 6 Machine Learning: Lecture 5: Decision trees (continued)
- 7 Machine Learning: Lecture 6a: Decision trees (continued)
- 8 Machine Learning: Lecture 6b: Linear Models
- 9 Machine Learning: Lecture 7a: Linear Classifier Expressiveness
- 10 Machine Learning: Lecture 7b: How good is a learning algorithm?
- 11 Machine Learning: Lecture 7c: Online Learning
- 12 Machine Learning: Lecture 8: Mistake Bound Learning
- 13 Machine Learning: Lecture 9a: On representations and learning
- 14 Machine Learning: Lecture 9b: Perceptron
- 15 Machine Learning: Lecture 10: Perceptron mistake bound
- 16 Machine Learning: Lecture 11: Linear Regression
- 17 Machine Learning: Lecture 12a: Introduction to Computational Learning Theory
- 18 Machine learning: Lecture 12b: PAC learning introduction
- 19 Machine learning: Lecture 13a: PAC learning
- 20 Machine Learning: Lecture 13b: Occam's Razor for consistent learners
- 21 Machine Learning: Lecture 14a: Occam's Razor (continued)
- 22 Machine Learning: Lecture 14b: Positive and Negative Learnability Results
- 23 Machine Learning: Lecture 15a: Positive and Negative Learnability Results
- 24 Machine Learning: Lecture 16: Agnostic Learning
- 25 Machine learning: Lecture 17: Shattering
- 26 Machine Learning: Lecture 18a: The VC Dimension
- 27 Machine Learning: Lecture 18b: Boosting
- 28 Machine Learning: Lecture 19: Boosting & Ensembles
- 29 Machine Learning: Lecture 20: Support Vector Machines
- 30 Machine learning: Lecture 21a: SVMs (continued)
- 31 Machine learning: Lecture 21b: Stochastic Gradient Descent for SVMs
- 32 Machine Learning: Lecture 22a: SGD for SVMs
- 33 Machine Learning: Lecture 22b: Loss Minimization
- 34 Machine Learning: Lecture 23: Bayesian Learning
- 35 Machine Learning: Lecture 24a: Maximum Likelihood Estimation for Regression
- 36 Machine Learning: Lecture 24b: Logistic regression
- 37 Machine Learning: Lecture 25: Neural Networks
- 38 Machine Learning: Lecture 26: Backpropagation
- 39 Machine Learning: Lecture 27a: Practical issues with Neural Networks
- 40 Machine Learning: Lecture 27b: Generative & Discriminative learning
- 41 Machine Learning: Lecture 28: Practical advice for building machine learning applications
