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Mastering Machine Learning Theory and Practice

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Getting started

Introduction to the Course

Introduction

The Basic Idea of Machine Learning History of Machine Learning Mathematical Formulation of the Basic Learning Problem Nonlinear Regression in High-Dimensions Recent Advances No Free Lunch, but Worth the Bite Quiz

Scientific Programming with Python

Programming Environment Basic Language Elements Code Efficiency and Vectorization Data Handling Image Processing and Convolutional Filters Quiz

Machine Learning with Sklearn

Overview Classification Using Multiple Machine Learning Models Performance Measures and Evaluations Data Handling Dimensionality Reduction, Feature Selection, and t-SNE Decision Trees and Random Forests Linear Support Vector Machines (SVMs)Nonlinear Support Vector Machines (SVMs)Validation of Support Vector Machines (SVMs)Challenge: Which Classifier Works Well?Solution: Which Classifier Works Well?Quiz

Neural Networks and Keras

Neurons and the Threshold Perceptron Multilayer Perceptron (MLP) and Keras Applying MLP to the MNIST dataset Representational Learning Introduction to Convolutional Neural Networks (CNNs)Applications of Convolutional Neural Networks (CNNs)Object Detection Challenges in Machine Learning Challenge: Choose the Best Model Solution: Choose the Best Model Quiz

Regression and Optimization

Linear Regression and Gradient Descent Error Surface and Challenges for Gradient Descent Advanced Gradient Optimization (Learning)Regularization: Ridge Regression and LASSO Nonlinear Regression Applying Gradient Descent to Neural Network Models Stochastic Gradient Descent (SGD)Artificial Neural Networks Implementing a Multilayer Perceptron for the XOR Problem Automatic Differentiation Quiz

Basic Probability Theory

Random Numbers and Their Probability (Density) Function Moments: Mean and Variance Examples of Probability (Density) Functions Cumulative Probability Function and the Central Limit Theorem Density Functions of Multiple Random Variables How to Combine Prior Knowledge With New Evidence Markov Chain Monte Carlo Quiz

Probabilistic Regression and Bayes Nets

Probabilistic Models Learning in Probabilistic Models: Maximum Likelihood Estimate Probabilistic Classification Maximum A Posteriori (MAP) and Regularization with Priors Bayes' Nets: Multivariate Causal Modeling Probabilistic and Stochastic Neural Networks Quiz

Generative Models

Modeling Classes

Supervised Generative Models

Self-Supervised Generative Models

Generative Neural Networks

Challenge: Sentiment Analysis Using Naive Bayes

Solution: Sentiment Analysis Using Naive Bayes

Challenge: K-Means Clustering

Solution: K-Means Clustering

Cyclic Models and Recurrent Neural Networks

Overview Sequence Processing Basic Sequence Processing with MLP and RNN Gated RNN and NLP Other Gated Architectures and Attention Markov Random Field Model and the Hopfield Model The Boltzmann Machine The Restricted Boltzmann Machine Quiz

Reinforcement Learning

Formalization of the T-Maze Problem Representation of the T-Maze roblem Model-Based Reinforcement Learning Model-Free Reinforcement Learning Deep Reinforcement Learning Actors and Actor-Critics Reinforcement Learning in the Brain Quiz

Artificial intelligence, the Brain, and Our Society

Overview Different Levels of Modeling and the Brain Machine Learning and Artificial Intelligence The Impact of Machine Learning Technology on Society

Conclusion

Wrap Up Copyright Information

Challenge: K-Means Clustering

Use k-means clustering to separate data points into three groups.

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Challenge

Challenge

The unsupervised machine learning algorithm $K$ -means clustering is a very popular algorithm in the world of data science. Even with little information about the data, it’s a fast and efficient algorithm for categorizing data points into groups.

Synthetically generated randomized data containing three separate point clusters is provided (see figure below). The cluster centers, represented as crosses, are also randomly initialized.

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