Machine Learning Essentials: from Theory to Practice
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Learn more
🎯 Start here
Self-assessment exercise - questions.pdf
Self-assessment exercise - solutions.pdf
👋🏼 Introductions, History of ML, and Course Overview
lec 01 - handout.pdf
Welcome 🤗
Who are we?
What is intelligence?
What is machine learning?
What machine learning is not
Types of machine learning
A brief history of machine learning
An overview of machine learning applications
Timetable and course logistics
Quiz
🏘️ Nearest Neighbors
lec 02 - handout.pdf
Overview
What is data?
Redundancies in data
How is data represented?
Datasets of training and testing
Overview of the nearest neighbors algorithm
L-p norms and cosine similarity
The nearest neighbor algorithm
The k-nearest neighbors algorithm
Cross-validation & generalization
On the pitfalls of k-NN & summary
Quiz
🌲 Decision Trees
lec 03 - handout.pdf
Introduction
An overview of decision trees
On the decision boundaries of decision trees
Discrete decision trees
On the expressiveness of decision trees
Intuition for learning decision trees
Uncertainty quantification, entropy, & information gain
Learning decision trees in action
Summary of decision trees
Quiz
🌐 Generalization & Ensembling
lec 04 - handout.pdf
Motivation
The bias-variance tradeoff
Ensembling overview & discussion
Bagging
Boosting
Summary & discussion
Quiz
📈 Linear Regression
lec 05 - handout.pdf
Motivation
A modular approach to learning
Regression step #1 - defining the model
Loops vs vectorized code
Regression step #2 - defining the loss
Regression step #3 - optimizing the loss
Analytical solution to regression
Iterative solution to regression (gradient descent)
Polynomial and regularized regression
Summary & discussion
Quiz
🕵🏼♂️ Linear Classification & Perceptron
lec 06 - handout.pdf
Motivation
Formulating linear classifiers
Loss functions for linear classification
Precision and recall
The Perceptron algorithm
On the existence and identification of nonlinear transformations
Multi-class classification + discussion
Quiz
〰️ Logistic Regression
lec 07 - handout.pdf
Review of linear regression and linear classification
Linear classification via thresholded activations
Linear classification via sigmoidal activations
Summary of attempts to linear classification
Probabilistic interpretation of logistic regression
Maximum Likelihood Estimation for logistic regression
Summary & discussion
Quiz
📏 Support Vector Machines
lec 08 - handout.pdf
Introducing Bernhard Schölkopf
Motivation (what makes a good linear classifier?)
Intuition for max-margin classification
Formulating max-margin classification
Duality and Lagrangian multipliers
Learning linear SVMs
SVMs with slack variables
Nonlinear SVMs
Summary
Quiz
🧠 Neural Networks
lec 09 & 10 - handout.pdf
Motivation
On the linear separability of NOT, AND, & XOR logic gates
Hand-designed feature engineering
Biological, computational, and artificial neurons
Feed-forward neural networks
Hierarchical feature learning
Discussion
Quiz
🔄 Backpropogation
lec 09 & 10 - handout.pdf
Mid-class review
On the expressivity of neural networks
Solving XOR using neural networks
An overview of backpropogation
Backpropogation example #1
Multivariate chain rule
Backpropogation example #2
Putting it all together: the backpropogation algorithm
Discussion
Quiz
📷 Convolutional Neural Networks - part I
lec 11 - handout.pdf
Introduction
Neural networks for computer vision
1D convolutions
2D convolutions
An intuition for convolutions
Mid-class review
Convolutions as layers
Example convolutions & Toeplitz matrices
On the hyperparameters of convolutions hparams
Pooling layers
1x1 convolutions and their usecases
Quiz
📸 Convolutional Neural Networks - part II
lec 12 - handout.pdf
Motivation
Data for computer vision
Compute for computer vision
Architectures for computer vision
Metrics for computer vision
Training tips & tricks
Discussion
Quiz
💭 Natural Language Processing - part I
lec 13 - handout.pdf
Motivation
N-gram language models
Distributional embeddings
Neural n-gram models
Evaluations
Discussion
Quiz
💬 Natural Language Processing - part II
lec 14 - handout.pdf
Review & overview
Recurrent Neural Networks example #1
Recurrent Neural Networks example #2
Recurrent Neural Networks over words & characters
Exploding and vanishing gradients
Long Short-Term Memory models (LSTMs)
Chaining recurrent cells
Transformers and their applications
Summary
Quiz
🌤️ Probabilistic Models: Discriminative vs Generative
lec 15 - handout.pdf
Introduction
Probability vs likelihood
Maximum Likelihood Estimation for Bernoullis
Maximum Likelihood Estimation for Gaussians
Maximum Likelihood Estimation in past models
Discriminative vs generative models
An overview of Bayes classifiers
On the risk of a classifier
Gaussian discriminant analysis intuition
Gaussian discriminant analysis learning, inference, and decision boundaries
Naive Bayes & summary
Quiz
🔗 Clustering and Density Estimation: k-Means and Mixtures of Gaussians
lec 16 - handout.pdf
Motivation
K-means overview
K-means for segmentation & quantization
K-means objective, loss, and challenges
Soft K-means
Mixture of Gaussians intuition
Mixture of Gaussians in higher dimensions
Mixture of Gaussians formalities
Expectation Maximization & discussion
Quiz
🔥 Dimensionality Reduction: Principal Component Analysis
lec 17 - handout.pdf
Motivation
Formalizing dimensionality reduction
Vectors, spaces, norms, orthogonality, and independence
Matrices as linear transformations
Eigenvalues and eigenvectors
Eigendecomposition of the covariance matrix
Step-by-step Principal Component Analysis (PCA)
Two interpretations of PCA
Applications of PCA
Random projections & discussion
Quiz
🤖 Reinforcement Learning
lec 18 - handout.pdf
Motivation for Reinforcement Learning (RL)
Formulating RL - part 1 (policies)
Formulating RL - part 2 (value functions)
Examples of RL systems
Formulating and solving example MDPs
Solving MDPs: value iteration and policy iteration
Exploration & exploitation & discussion
Quiz
Student Course Perceptions & class photo
✍️ Self-guided Assignments
Assignment 1/3.pdf
Assignment 2/3.pdf
Assignment 3/3.pdf
Products
Course
Section
📈 Linear Regression
📈 Linear Regression
Machine Learning Essentials: from Theory to Practice
Buy now
Learn more
🎯 Start here
Self-assessment exercise - questions.pdf
Self-assessment exercise - solutions.pdf
👋🏼 Introductions, History of ML, and Course Overview
lec 01 - handout.pdf
Welcome 🤗
Who are we?
What is intelligence?
What is machine learning?
What machine learning is not
Types of machine learning
A brief history of machine learning
An overview of machine learning applications
Timetable and course logistics
Quiz
🏘️ Nearest Neighbors
lec 02 - handout.pdf
Overview
What is data?
Redundancies in data
How is data represented?
Datasets of training and testing
Overview of the nearest neighbors algorithm
L-p norms and cosine similarity
The nearest neighbor algorithm
The k-nearest neighbors algorithm
Cross-validation & generalization
On the pitfalls of k-NN & summary
Quiz
🌲 Decision Trees
lec 03 - handout.pdf
Introduction
An overview of decision trees
On the decision boundaries of decision trees
Discrete decision trees
On the expressiveness of decision trees
Intuition for learning decision trees
Uncertainty quantification, entropy, & information gain
Learning decision trees in action
Summary of decision trees
Quiz
🌐 Generalization & Ensembling
lec 04 - handout.pdf
Motivation
The bias-variance tradeoff
Ensembling overview & discussion
Bagging
Boosting
Summary & discussion
Quiz
📈 Linear Regression
lec 05 - handout.pdf
Motivation
A modular approach to learning
Regression step #1 - defining the model
Loops vs vectorized code
Regression step #2 - defining the loss
Regression step #3 - optimizing the loss
Analytical solution to regression
Iterative solution to regression (gradient descent)
Polynomial and regularized regression
Summary & discussion
Quiz
🕵🏼♂️ Linear Classification & Perceptron
lec 06 - handout.pdf
Motivation
Formulating linear classifiers
Loss functions for linear classification
Precision and recall
The Perceptron algorithm
On the existence and identification of nonlinear transformations
Multi-class classification + discussion
Quiz
〰️ Logistic Regression
lec 07 - handout.pdf
Review of linear regression and linear classification
Linear classification via thresholded activations
Linear classification via sigmoidal activations
Summary of attempts to linear classification
Probabilistic interpretation of logistic regression
Maximum Likelihood Estimation for logistic regression
Summary & discussion
Quiz
📏 Support Vector Machines
lec 08 - handout.pdf
Introducing Bernhard Schölkopf
Motivation (what makes a good linear classifier?)
Intuition for max-margin classification
Formulating max-margin classification
Duality and Lagrangian multipliers
Learning linear SVMs
SVMs with slack variables
Nonlinear SVMs
Summary
Quiz
🧠 Neural Networks
lec 09 & 10 - handout.pdf
Motivation
On the linear separability of NOT, AND, & XOR logic gates
Hand-designed feature engineering
Biological, computational, and artificial neurons
Feed-forward neural networks
Hierarchical feature learning
Discussion
Quiz
🔄 Backpropogation
lec 09 & 10 - handout.pdf
Mid-class review
On the expressivity of neural networks
Solving XOR using neural networks
An overview of backpropogation
Backpropogation example #1
Multivariate chain rule
Backpropogation example #2
Putting it all together: the backpropogation algorithm
Discussion
Quiz
📷 Convolutional Neural Networks - part I
lec 11 - handout.pdf
Introduction
Neural networks for computer vision
1D convolutions
2D convolutions
An intuition for convolutions
Mid-class review
Convolutions as layers
Example convolutions & Toeplitz matrices
On the hyperparameters of convolutions hparams
Pooling layers
1x1 convolutions and their usecases
Quiz
📸 Convolutional Neural Networks - part II
lec 12 - handout.pdf
Motivation
Data for computer vision
Compute for computer vision
Architectures for computer vision
Metrics for computer vision
Training tips & tricks
Discussion
Quiz
💭 Natural Language Processing - part I
lec 13 - handout.pdf
Motivation
N-gram language models
Distributional embeddings
Neural n-gram models
Evaluations
Discussion
Quiz
💬 Natural Language Processing - part II
lec 14 - handout.pdf
Review & overview
Recurrent Neural Networks example #1
Recurrent Neural Networks example #2
Recurrent Neural Networks over words & characters
Exploding and vanishing gradients
Long Short-Term Memory models (LSTMs)
Chaining recurrent cells
Transformers and their applications
Summary
Quiz
🌤️ Probabilistic Models: Discriminative vs Generative
lec 15 - handout.pdf
Introduction
Probability vs likelihood
Maximum Likelihood Estimation for Bernoullis
Maximum Likelihood Estimation for Gaussians
Maximum Likelihood Estimation in past models
Discriminative vs generative models
An overview of Bayes classifiers
On the risk of a classifier
Gaussian discriminant analysis intuition
Gaussian discriminant analysis learning, inference, and decision boundaries
Naive Bayes & summary
Quiz
🔗 Clustering and Density Estimation: k-Means and Mixtures of Gaussians
lec 16 - handout.pdf
Motivation
K-means overview
K-means for segmentation & quantization
K-means objective, loss, and challenges
Soft K-means
Mixture of Gaussians intuition
Mixture of Gaussians in higher dimensions
Mixture of Gaussians formalities
Expectation Maximization & discussion
Quiz
🔥 Dimensionality Reduction: Principal Component Analysis
lec 17 - handout.pdf
Motivation
Formalizing dimensionality reduction
Vectors, spaces, norms, orthogonality, and independence
Matrices as linear transformations
Eigenvalues and eigenvectors
Eigendecomposition of the covariance matrix
Step-by-step Principal Component Analysis (PCA)
Two interpretations of PCA
Applications of PCA
Random projections & discussion
Quiz
🤖 Reinforcement Learning
lec 18 - handout.pdf
Motivation for Reinforcement Learning (RL)
Formulating RL - part 1 (policies)
Formulating RL - part 2 (value functions)
Examples of RL systems
Formulating and solving example MDPs
Solving MDPs: value iteration and policy iteration
Exploration & exploitation & discussion
Quiz
Student Course Perceptions & class photo
✍️ Self-guided Assignments
Assignment 1/3.pdf
Assignment 2/3.pdf
Assignment 3/3.pdf
12 Lessons
lec 05 - handout.pdf
Motivation
A modular approach to learning
Regression step #1 - defining the model
Loops vs vectorized code
Regression step #2 - defining the loss
Regression step #3 - optimizing the loss
Analytical solution to regression
Iterative solution to regression (gradient descent)
Polynomial and regularized regression
Summary & discussion
Quiz