Introduction
Welcome
Machine Learning
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Guidelines and tips
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Machine Learning: Exploring Computer Learning's Capabilities and Applications, Thesis of Public finance
An introduction to machine learning, a field of study that enables computers to learn without being explicitly programmed. Andrew ng discusses the growth of machine learning from work in ai and its new capabilities for computers. Examples of machine learning applications include database mining, autonomous helicopters, handwriting recognition, natural language processing, computer vision, self-customizing programs, and understanding human learning. The document also covers machine learning algorithms such as supervised learning and unsupervised learning, and provides practical advice for applying learning algorithms.
Typology: Thesis
2018/2019
1 / 39
This page cannot be seen from the preview
Don't miss anything!
Related documents
Partial preview of the text
Download Machine Learning: Exploring Computer Learning's Capabilities and Applications and more Thesis Public finance in PDF only on Docsity!
Introduction^ Welcome
Machine Learning
SPAM
Machine Learning
- Grew out of work in AI- New capability for computers Examples: - Database mining
Large datasets from growth of automation/web.E.g., Web click data, medical records, biology, engineering
- Applications can’t program by hand.
E.g., Autonomous helicopter, handwriting recognition, most ofNatural Language Processing (NLP), Computer Vision.
Machine Learning
- Grew out of work in AI- New capability for computers Examples: - Database mining
Large datasets from growth of automation/web.E.g., Web click data, medical records, biology, engineering
- Applications can’t program by hand.
E.g., Autonomous helicopter, handwriting recognition, most ofNatural Language Processing (NLP), Computer Vision.
Machine Learning
- Grew out of work in AI- New capability for computers Examples: - Database mining
Large datasets from growth of automation/web.E.g., Web click data, medical records, biology, engineering
- Applications can’t program by hand.
E.g., Autonomous helicopter, handwriting recognition, most ofNatural Language Processing (NLP), Computer Vision.
- Self-customizing programs
E.g., Amazon, Netflix product recommendations
Introduction^ What is machinelearning
Machine Learning
-^
Machine Learning definitionArthur Samuel (1959). Machine Learning: Field ofstudy that gives computers the ability to learnwithout being explicitly programmed.
-^
Machine Learning definitionArthur Samuel (1959). Machine Learning: Field ofstudy that gives computers the ability to learnwithout being explicitly programmed.
Classifying emails as spam or not spam.Watching you label emails as spam or not spam.The number (or fraction) of emails correctly classified as spam/not spam.None of the above—this is not a machine learning problem.
Suppose your email program watches which emails you do or donot mark as spam, and based on that learns how to better filterspam. What is the task T in this setting?
“A computer program is said to
learn
from experience E with respect to
some task T and some performance measure P, if its performance on T,as measured by P, improves with experience E.”
Classifying emails as spam or not spam.Watching you label emails as spam or not spam.The number (or fraction) of emails correctly classified as spam/not spam.None of the above—this is not a machine learning problem.
Suppose your email program watches which emails you do or donot mark as spam, and based on that learns how to better filterspam. What is the task T in this setting?
“A computer program is said to
learn
from experience E with respect to
some task T and some performance measure P, if its performance on T,as measured by P, improves with experience E.”
Machine learning algorithms:- Supervised learning- Unsupervised learningOthers: Reinforcement learning, recommender
systems. Also talk about: Practical advice for applying
learning algorithms.