Topic

 

Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

from Machine Learning - Wikipedia

This topic includes the following resources and journeys:

 

 

Learning From Data

Professor Yaser Abu-Mostafa
Beginner
Course
Theory

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical...

See More

Data-Driven Dynamical Systems Overview

Steve Brunton
21 min
Intermediate
Video
Theory

This video provides a high-level overview of this new series on data-driven dynamical systems. In particular, we explore the various challenges in modern dynamical systems, along with...

See More

Artificial Intelligence

Steve Brunton
6 min
Beginner
Video
Theory

This lecture discusses artificial intelligence (AI) in the context of data science and machine learning.

See More

Yann LeCun’s Deep Learning Course at CDS

Yann LeCun, Alfredo Canziani
Intermediate
Course
Theory

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning...

See More

An Artificial Intelligence Primer

Arman Molki | Quanser
15 min
Beginner
Article / Blog
Theory

This blog post is a great primer providing definitions for basic terms used in AI and machine learning (ML) such as supervised learning, unsupervised learning, and transfer learning...

See More

Full Stack Deep Learning

Sergey Karayev, Josh Tobin, Pieter Abbeel
Intermediate
Course
Theory

There are many great courses to learn how to train deep neural networks. However, training the model is just one part of shipping a deep learning project. This course teaches full-stack...

See More

Machine Learning Control: Overview

Steve Brunton
10 min
Beginner
Video
Theory

This lecture provides an overview of how to use machine learning optimization directly to design control laws, without the need for a model of the dynamics.

See More

Machine Learning Overview

Steve Brunton
7 min
Beginner
Video
Theory

This lecture provides an overview of machine learning, and how it fits into this introductory video sequence on data science. We discuss how machine learning involves "modeling with data".

See More

Machine Learning Goals

Steve Brunton
7 min
Beginner
Video
Theory

This lecture discusses the high-level goals of machine learning, and what we want out of our models. Goals include speed and accuracy, along with interpretability, generalizability...

See More