Lectures
You can download the lectures here. We will try to upload the annotated classroom slides after the corresponding classes.
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(foml-01) ML and Learning Paradigms
tl;dr: What is Machine Learning and different learning paradigms.
[slides]
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(foml-02) Probability Refresher - 1
tl;dr: Probability, sum rule, product rule, random variable, distribution, marginal, conditional, Bayes rule.
[slides] [annotated-slides]
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(foml-03) Probability Refresher - 2
tl;dr: Expectation, Variance of a random variable and the Gausssian distribution.
[slides] [annotated-slides]
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(foml-04) MLE
tl;dr: Maximum likelihood principle - estimating the most likely explanation of the data.
[slides] [annotated-slides]
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(foml-05) MAP and Fully Bayesian Treatment
tl;dr: Maximum likelihood principle - estimating the most likely explanation of the data.
[slides] [annotated-slides]
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(foml-06) Linear Regression with Basis functions
tl;dr: First supervised learning model for regression - weighted sum of basis functions.
[slides] [annotated-slides]
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(foml-07) Geometric Interpretation for Least Squares
tl;dr: Project of targets onto the space spanned by the basis functions.
[slides] [annotated-slides]
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(foml-08) SGD
tl;dr: Sequential learning of the parameters via the gradient of the loss funciton.
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(foml-09) Regularized Least Sqaures
tl;dr: How to address the overfitting?
[slides] [annotated-slides]
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(foml-10) Bias Variance Decomposition
tl;dr: Breaking down a model's prediction error into components.
[slides] [annotated-slides]
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(foml-11) Bayesian Regression
tl;dr: Model averaging in the parameter space.
[slides] [annotated-slides]
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(foml-12) Decision Theory
tl;dr: Strategies to build classifiers.
[slides] [annotated-slides]
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(foml-13) Probabilistic Generative Models
tl;dr: Models that learn the underlying data distribution, not just classify data.
Suggested Reading