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Lecture 11: Computational Learning Theory
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Classroom Contents
Machine Learning - Spring 2025
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- 1 Lecture 26: Neural networks (continued)
- 2 Lecture 25: Neural networks (continued)
- 3 Lecture 24b: Neural networks
- 4 Lecture 24a: Loss minimization (revisited)
- 5 Lecture 23b: Logistic regression
- 6 Lecture 23a: Bayesian learning (continued)
- 7 Lecture 22b: Introduction to Bayesian learning
- 8 Lecture 22a: Learning as loss minimization
- 9 Lecture 21: Stochastic Gradient Descent for SVM
- 10 Lecture 20: Practical machine learning tutorial
- 11 Lecture 19: SVMs (continued)
- 12 Lecture 18a: Boosting and Ensembles (continued)
- 13 Lecture 18b: Support vector machines
- 14 Lecture 17: Boosting
- 15 Lecture 16: VC dimensions (continued)
- 16 Lecture 15: VC dimension
- 17 Lecture 14: Agnostic learning
- 18 Lecture 13: Learnability Results for Consistent Learners
- 19 Lecture 12: Occam's Razor for a Consistent Learner
- 20 Lecture 11: Computational Learning Theory
- 21 Lecture 10: Least Mean Squares Regression
- 22 Lecture 9: Perceptron (continued)
- 23 Lecture 8b: The Perceptron Algorithm
- 24 Lecture 8a: Mistake bound learning (continued)
- 25 Lecture 7: The mistake bound model
- 26 Lecture 6b: Quantifying learning algorithms
- 27 Lectures 6a: Linear models expressiveness
- 28 Lecture 5b: Linear Models
- 29 Lecture 5a: Overfitting
- 30 Lecture 4: Decision trees (continued)
- 31 Lecture 3: Decision trees
- 32 Lecture 2: Supervised Learning - The setup
- 33 Lecture 1: What is Machine Learning?
- 34 Lecture 27: Practical advice for using machine learning
