Submit Search
Upload
Chapter01.ppt
•
Download as PPT, PDF
•
0 likes
•
307 views
B
butest
Follow
Report
Share
Report
Share
1 of 30
Download now
Recommended
Hard Exercise Set information
Hard Exercise Set information
butest
Machine Learning Introductory slides
Lecture 1.pptx
Lecture 1.pptx
Makerere Unversity School of Public Health, Victoria University
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
An overview of machine learning
An overview of machine learning
piyush Kumar Sharma
Basics of Machine Learning
Ml introduction
Ml introduction
RanjithaM32
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data. Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Machine learning overview
Machine learning overview
prih_yah
Definition of Machine Learning Type of Machine Learning: Classification Regression Supervised Learning Unsupervised Learning Reinforcement Learning Supervised Learning: Supervised Classification Training set Hypothesis class Empirical error Margin Noise Inductive bias Generalization Model assessment Cross-Validation Classification in NLP Types of Classification
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Marina Santini
Simulation, teaching and learning using simulation, motivation through simulation, prediction through simulation, advantages and disadvantages of simulation, suitability for implementation of simulation in Malaysia
Simulation Report
Simulation Report
Lim1990
(Machine)Learning with limited labels(Machine)Learning with limited labels(Machine)Learning with limited labels, Alexandria Workshop 2017
(Machine)Learning with limited labels(Machine)Learning with limited labels(Ma...
(Machine)Learning with limited labels(Machine)Learning with limited labels(Ma...
Eirini Ntoutsi
Recommended
Hard Exercise Set information
Hard Exercise Set information
butest
Machine Learning Introductory slides
Lecture 1.pptx
Lecture 1.pptx
Makerere Unversity School of Public Health, Victoria University
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
An overview of machine learning
An overview of machine learning
piyush Kumar Sharma
Basics of Machine Learning
Ml introduction
Ml introduction
RanjithaM32
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data. Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Machine learning overview
Machine learning overview
prih_yah
Definition of Machine Learning Type of Machine Learning: Classification Regression Supervised Learning Unsupervised Learning Reinforcement Learning Supervised Learning: Supervised Classification Training set Hypothesis class Empirical error Margin Noise Inductive bias Generalization Model assessment Cross-Validation Classification in NLP Types of Classification
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Marina Santini
Simulation, teaching and learning using simulation, motivation through simulation, prediction through simulation, advantages and disadvantages of simulation, suitability for implementation of simulation in Malaysia
Simulation Report
Simulation Report
Lim1990
(Machine)Learning with limited labels(Machine)Learning with limited labels(Machine)Learning with limited labels, Alexandria Workshop 2017
(Machine)Learning with limited labels(Machine)Learning with limited labels(Ma...
(Machine)Learning with limited labels(Machine)Learning with limited labels(Ma...
Eirini Ntoutsi
Talk given by Jitender Chauhan, Sr. Software Engineer at Salesforce, at Tech Time Meetup in June 2016
Introduction to Deep Learning
Introduction to Deep Learning
Salesforce Engineering
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ 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 is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Machine learning basics
Machine learning basics
Akanksha Bali
Machine Learning extensive introduction presented by Raluca Aposton in GibDevs meetup April 2020
Choosing a Machine Learning technique to solve your need
Choosing a Machine Learning technique to solve your need
GibDevs
Induction, Induction pipeline, Training set, test set, development set, Parameters, Hyperparameters, Accuracy, precision, recall, f-measure, Confusion matrix, Crossvalidation, Leave one out, Stratification
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Marina Santini
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
Simulation Powerpoint- Lecture Notes
Simulation Powerpoint- Lecture Notes
Kesavartinii Bala Krisnain
This session covers basics of machine learning and mapping it to real-world scenarios.
Understanding Basics of Machine Learning
Understanding Basics of Machine Learning
Pranav Ainavolu
Full download : https://goo.gl/vDNh5Y Quantitative Analysis For Management 13th Edition Render Solutions Manual
Quantitative Analysis For Management 13th Edition Render Solutions Manual
Quantitative Analysis For Management 13th Edition Render Solutions Manual
StricklandMaxines
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data. Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and highly unpredicted sources. The 4Vs of Big Data ● Volume - Scale of Data ● Velocity - Analysis of Streaming Data ● Variety - Different forms of Data ● Veracity - Uncertainty of Data With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics. Taking it a step further to further make futuristic prediction and realistic inferences - the concept of Machine Learning. A blend of both gives a robust analysis of data for the past, now and the future. There is a thin line between data analytics and Machine learning which becomes very obvious when you dig deep.
Introduction to machine learning
Introduction to machine learning
Adetimehin Oluwasegun Matthew
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
林守德/Practical Issues in Machine Learning
林守德/Practical Issues in Machine Learning
台灣資料科學年會
课堂讲义(最后更新:2009-9-25)
课堂讲义(最后更新:2009-9-25)
butest
Data science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. Data science community is made of people coming from different areas, and who do not always understand each others. Everyone is using his own concepts and not always understands how these map when applied to other techniques. In particular, Machine Learning experts do not always understand how Decision Optimization concepts maps or differs from their own concepts.
Machine Learning vs Decision Optimization comparison
Machine Learning vs Decision Optimization comparison
Alain Chabrier
machine learning, features engineering
Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)
Jeet Das
machine learning introduction
ML_Lecture_1.ppt
ML_Lecture_1.ppt
RashiAgarwal839124
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will: - Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape - Demonstrate Azure Databricks’ capabilities for building custom machine learning models - Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Machine Learning with Azure and Databricks Virtual Workshop
Machine Learning with Azure and Databricks Virtual Workshop
CCG
Engineering Intelligent System using Machine Learning
Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning
Saurabh Kaushik
The outline of our course on supervised machine learning. - Laszlo kovacs Arpad Fulop
Machine learning at b.e.s.t. summer university
Machine learning at b.e.s.t. summer university
László Kovács
Learn how machine learning and AI technologies can be immediately applied to eCommerce businesses.
Machine Learning in e commerce - Reboot
Machine Learning in e commerce - Reboot
Marion DE SOUSA
original
original
butest
Basic Notions of Learning, Introduction to Learning ...
Basic Notions of Learning, Introduction to Learning ...
butest
overview of ML and DA
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
cloudserviceuit
PyData SF 2016 --- Moving forward through the darkness
PyData SF 2016 --- Moving forward through the darkness
PyData SF 2016 --- Moving forward through the darkness
Chia-Chi Chang
machine learning
Machine_Learning.pptx
Machine_Learning.pptx
shubhamatak136
More Related Content
What's hot
Talk given by Jitender Chauhan, Sr. Software Engineer at Salesforce, at Tech Time Meetup in June 2016
Introduction to Deep Learning
Introduction to Deep Learning
Salesforce Engineering
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ 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 is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Machine learning basics
Machine learning basics
Akanksha Bali
Machine Learning extensive introduction presented by Raluca Aposton in GibDevs meetup April 2020
Choosing a Machine Learning technique to solve your need
Choosing a Machine Learning technique to solve your need
GibDevs
Induction, Induction pipeline, Training set, test set, development set, Parameters, Hyperparameters, Accuracy, precision, recall, f-measure, Confusion matrix, Crossvalidation, Leave one out, Stratification
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Marina Santini
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
Simulation Powerpoint- Lecture Notes
Simulation Powerpoint- Lecture Notes
Kesavartinii Bala Krisnain
This session covers basics of machine learning and mapping it to real-world scenarios.
Understanding Basics of Machine Learning
Understanding Basics of Machine Learning
Pranav Ainavolu
Full download : https://goo.gl/vDNh5Y Quantitative Analysis For Management 13th Edition Render Solutions Manual
Quantitative Analysis For Management 13th Edition Render Solutions Manual
Quantitative Analysis For Management 13th Edition Render Solutions Manual
StricklandMaxines
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data. Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and highly unpredicted sources. The 4Vs of Big Data ● Volume - Scale of Data ● Velocity - Analysis of Streaming Data ● Variety - Different forms of Data ● Veracity - Uncertainty of Data With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics. Taking it a step further to further make futuristic prediction and realistic inferences - the concept of Machine Learning. A blend of both gives a robust analysis of data for the past, now and the future. There is a thin line between data analytics and Machine learning which becomes very obvious when you dig deep.
Introduction to machine learning
Introduction to machine learning
Adetimehin Oluwasegun Matthew
What's hot
(8)
Introduction to Deep Learning
Introduction to Deep Learning
Machine learning basics
Machine learning basics
Choosing a Machine Learning technique to solve your need
Choosing a Machine Learning technique to solve your need
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Simulation Powerpoint- Lecture Notes
Simulation Powerpoint- Lecture Notes
Understanding Basics of Machine Learning
Understanding Basics of Machine Learning
Quantitative Analysis For Management 13th Edition Render Solutions Manual
Quantitative Analysis For Management 13th Edition Render Solutions Manual
Introduction to machine learning
Introduction to machine learning
Similar to Chapter01.ppt
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
林守德/Practical Issues in Machine Learning
林守德/Practical Issues in Machine Learning
台灣資料科學年會
课堂讲义(最后更新:2009-9-25)
课堂讲义(最后更新:2009-9-25)
butest
Data science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. Data science community is made of people coming from different areas, and who do not always understand each others. Everyone is using his own concepts and not always understands how these map when applied to other techniques. In particular, Machine Learning experts do not always understand how Decision Optimization concepts maps or differs from their own concepts.
Machine Learning vs Decision Optimization comparison
Machine Learning vs Decision Optimization comparison
Alain Chabrier
machine learning, features engineering
Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)
Jeet Das
machine learning introduction
ML_Lecture_1.ppt
ML_Lecture_1.ppt
RashiAgarwal839124
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will: - Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape - Demonstrate Azure Databricks’ capabilities for building custom machine learning models - Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Machine Learning with Azure and Databricks Virtual Workshop
Machine Learning with Azure and Databricks Virtual Workshop
CCG
Engineering Intelligent System using Machine Learning
Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning
Saurabh Kaushik
The outline of our course on supervised machine learning. - Laszlo kovacs Arpad Fulop
Machine learning at b.e.s.t. summer university
Machine learning at b.e.s.t. summer university
László Kovács
Learn how machine learning and AI technologies can be immediately applied to eCommerce businesses.
Machine Learning in e commerce - Reboot
Machine Learning in e commerce - Reboot
Marion DE SOUSA
original
original
butest
Basic Notions of Learning, Introduction to Learning ...
Basic Notions of Learning, Introduction to Learning ...
butest
overview of ML and DA
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
cloudserviceuit
PyData SF 2016 --- Moving forward through the darkness
PyData SF 2016 --- Moving forward through the darkness
PyData SF 2016 --- Moving forward through the darkness
Chia-Chi Chang
machine learning
Machine_Learning.pptx
Machine_Learning.pptx
shubhamatak136
MLlecture1.ppt
MLlecture1.ppt
butest
MLlecture1.ppt
MLlecture1.ppt
butest
What is Machine Learning? What problems does it try to solve? What are the main categories and fundamental concepts of Machine Learning systems?
Machine Learning Landscape
Machine Learning Landscape
Eng Teong Cheah
Machine Learning
Machine Learning.pptx
Machine Learning.pptx
chadhar227
Machine learning
UNIT 1 Machine Learning [KCS-055] (1).pptx
UNIT 1 Machine Learning [KCS-055] (1).pptx
RohanPathak30
In this slide I answer the basic questions about machine learning like: What is Machine Learning? What are the types of machine learning? How to deal with data? How to test model performance?
Machine learning introduction
Machine learning introduction
Anas Jamil
Similar to Chapter01.ppt
(20)
林守德/Practical Issues in Machine Learning
林守德/Practical Issues in Machine Learning
课堂讲义(最后更新:2009-9-25)
课堂讲义(最后更新:2009-9-25)
Machine Learning vs Decision Optimization comparison
Machine Learning vs Decision Optimization comparison
Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)
ML_Lecture_1.ppt
ML_Lecture_1.ppt
Machine Learning with Azure and Databricks Virtual Workshop
Machine Learning with Azure and Databricks Virtual Workshop
Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning
Machine learning at b.e.s.t. summer university
Machine learning at b.e.s.t. summer university
Machine Learning in e commerce - Reboot
Machine Learning in e commerce - Reboot
original
original
Basic Notions of Learning, Introduction to Learning ...
Basic Notions of Learning, Introduction to Learning ...
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
PyData SF 2016 --- Moving forward through the darkness
PyData SF 2016 --- Moving forward through the darkness
Machine_Learning.pptx
Machine_Learning.pptx
MLlecture1.ppt
MLlecture1.ppt
MLlecture1.ppt
MLlecture1.ppt
Machine Learning Landscape
Machine Learning Landscape
Machine Learning.pptx
Machine Learning.pptx
UNIT 1 Machine Learning [KCS-055] (1).pptx
UNIT 1 Machine Learning [KCS-055] (1).pptx
Machine learning introduction
Machine learning introduction
More from butest
EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
butest
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
butest
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
butest
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
butest
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
butest
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
butest
Com 380, Summer II
Com 380, Summer II
butest
PPT
PPT
butest
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
butest
MICHAEL JACKSON.doc
MICHAEL JACKSON.doc
butest
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
butest
Facebook
Facebook
butest
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
butest
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
butest
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
butest
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
butest
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
butest
Mac OS X Guide.doc
Mac OS X Guide.doc
butest
hier
hier
butest
WEB DESIGN!
WEB DESIGN!
butest
More from butest
(20)
EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
Com 380, Summer II
Com 380, Summer II
PPT
PPT
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
MICHAEL JACKSON.doc
MICHAEL JACKSON.doc
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
Facebook
Facebook
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
Mac OS X Guide.doc
Mac OS X Guide.doc
hier
hier
WEB DESIGN!
WEB DESIGN!
Chapter01.ppt
1.
Machine Learning (ML)
and Knowledge Discovery in Databases (KDD) Instructor: Rich Maclin [email_address] Texts: Machine Learning , Mitchell Notes based on Mitchell’s Lecture Notes
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Software that Customizes
to User
12.
13.
14.
15.
16.
17.
18.
Obtaining Training Examples
19.
20.
Design Choices
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
Download now