This document provides an overview of machine learning. It defines machine learning as using algorithms to allow computers to learn from empirical data. The document outlines different machine learning models, algorithms, and techniques including supervised learning, unsupervised learning, performance factors, and applications. It concludes that machine learning will become increasingly important and be applied to more solutions.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
We will review some modern machine learning applications, understand variety of machine learning problem definitions, go through particular approaches of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning experiment.
Le Machine Learning, l’IA, le DeepLearning, les Statistiques, le Data Mining… bref, tous ces mots sont les buzz words du moment mais que se cache-t-il derrière ?
A travers des exemples concrets, on parcourra les différentes approches du Machine Learning, les grandes familles d’algorithmes (n’ayez crainte : sans rentrer dans le cœur de leurs implémentations), puis les outils et les frameworks à la disposition des Data Scientists… et pour finir, on essayera de prédire l’avenir !
Salon Data - Nantes - 19 Septembre 2017
https://salondata.fr/2017/07/12/0930-1030-ml/
Introduction to machine learning and model building using linear regressionGirish Gore
An basic introduction of Machine learning and a kick start to model building process using Linear Regression. Covers fundamentals of Data Science field called Machine Learning covering the fundamental topic of supervised learning method called linear regression. Importantly it covers this using R language and throws light on how to interpret linear regression results of a model. Interpretation of results , tuning and accuracy metrics like RMSE Root Mean Squared Error are covered here.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
We will review some modern machine learning applications, understand variety of machine learning problem definitions, go through particular approaches of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning experiment.
Le Machine Learning, l’IA, le DeepLearning, les Statistiques, le Data Mining… bref, tous ces mots sont les buzz words du moment mais que se cache-t-il derrière ?
A travers des exemples concrets, on parcourra les différentes approches du Machine Learning, les grandes familles d’algorithmes (n’ayez crainte : sans rentrer dans le cœur de leurs implémentations), puis les outils et les frameworks à la disposition des Data Scientists… et pour finir, on essayera de prédire l’avenir !
Salon Data - Nantes - 19 Septembre 2017
https://salondata.fr/2017/07/12/0930-1030-ml/
Introduction to machine learning and model building using linear regressionGirish Gore
An basic introduction of Machine learning and a kick start to model building process using Linear Regression. Covers fundamentals of Data Science field called Machine Learning covering the fundamental topic of supervised learning method called linear regression. Importantly it covers this using R language and throws light on how to interpret linear regression results of a model. Interpretation of results , tuning and accuracy metrics like RMSE Root Mean Squared Error are covered here.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
MachineLearning for dummies with Python
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills?
If your response to both questions is 'Yes', we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
Machine Learning for Dummies (without mathematics)ActiveEon
It presents an introduction and the basic concepts of machine learning without mathematics. This is a short presentation for beginners in machine learning.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematicians with black-magic skills?
If your response to both questions is ‘Yes’, we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
This slide was used by Mr.Viju Chacko at FAYA:80 that gave a basic introduction to Ai. It act as an introduction to different terminologies related to AI that could enable its audience to understand the technology better.
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.
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.
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
機器學習速遊 (Quick Tour of Machine Learning)
機器學習旨在讓電腦能由資料中累積的經驗來自我進步,近年來已廣泛應用於資料探勘、計算機視覺、自然語言處理、生物特徵識別、搜尋引擎、醫學診斷、檢測信用卡欺詐、證券市場分析、DNA序列測序、語音和手寫識別、戰略遊戲和機器人等領域。它已成為資料科學的基礎學科之一,為任何資料科學家必備的工具。
這門課程將由台大資訊工程系林軒田教授利用短短的六個小時,快速地帶大家探索機器學習的基石、介紹核心的模型及一些熱門的技法,希望幫助大家有效率而紮實地了解這個領域,以妥善地使用各式機器學習的工具。此課程適合所有希望開始運用資料的資料分析者,推薦給所有有志於資料分析領域的資料科學愛好者。
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
MachineLearning for dummies with Python
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills?
If your response to both questions is 'Yes', we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
Machine Learning for Dummies (without mathematics)ActiveEon
It presents an introduction and the basic concepts of machine learning without mathematics. This is a short presentation for beginners in machine learning.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematicians with black-magic skills?
If your response to both questions is ‘Yes’, we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
This slide was used by Mr.Viju Chacko at FAYA:80 that gave a basic introduction to Ai. It act as an introduction to different terminologies related to AI that could enable its audience to understand the technology better.
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.
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.
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
機器學習速遊 (Quick Tour of Machine Learning)
機器學習旨在讓電腦能由資料中累積的經驗來自我進步,近年來已廣泛應用於資料探勘、計算機視覺、自然語言處理、生物特徵識別、搜尋引擎、醫學診斷、檢測信用卡欺詐、證券市場分析、DNA序列測序、語音和手寫識別、戰略遊戲和機器人等領域。它已成為資料科學的基礎學科之一,為任何資料科學家必備的工具。
這門課程將由台大資訊工程系林軒田教授利用短短的六個小時,快速地帶大家探索機器學習的基石、介紹核心的模型及一些熱門的技法,希望幫助大家有效率而紮實地了解這個領域,以妥善地使用各式機器學習的工具。此課程適合所有希望開始運用資料的資料分析者,推薦給所有有志於資料分析領域的資料科學愛好者。
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。
Top 5 Deep Learning and AI Stories - October 6, 2017NVIDIA
Read this week's top 5 news updates in deep learning and AI: Gartner predicts top 10 strategic technology trends for 2018; Oracle adds GPU Accelerated Computing to Oracle Cloud Infrastructure; chemistry and physics Nobel Prizes are awarded to teams supported by GPUs; MIT uses deep learning to help guide decisions in ICU; and portfolio management firms are using AI to seek alpha.
Machine learning is important because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations.
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.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Overview of Machine learning concepts – Over fitting and train/test splits, Types of Machine learning – Supervised, Unsupervised, Reinforced learning, Introduction to Bayes Theorem, Linear Regression- model assumptions, regularization (lasso, ridge, elastic net), Classification and Regression algorithms- Naïve Bayes, K-Nearest Neighbors, logistic regression, support vector machines (SVM), decision trees, and random forest, Classification Errors..
Selecting the Right Type of Algorithm for Various Applications - PhdassistancePhD Assistance
Machine learning algorithms may be classified mainly into three main types. Supervised learning constructs a mathematical model from the training data, including input and output labels. The techniques of data categorization and regression are deemed supervised learning. In unsupervised learning, the system constructs a model using just the input characteristics but no output labeling. The classifiers are then trained to search the dataset for a specific pattern.
Learn More:https://bit.ly/3sX9xuQ
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A comprehensive introduction to machine learning and deep learning along with application in finance (provided by an example of predicting bank failure). Then, the difference of ML in tech and ML in finance is outlined. Last section is excluded from the file.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
An overview of machine learning
1. An Overview of
Machine Learning
Speaker: Yi-Fan Chang
Adviser: Prof. J. J. Ding
Date: 2011/10/21
2. What is machine learning?
Learning system model
Training and testing
Performance
Algorithms
Machine learning structure
What are we seeking?
Learning techniques
Applications
Conclusion
Outline & Content
3. A branch of artificial intelligence, concerned with the
design and development of algorithms that allow
computers to evolve behaviors based on empirical data.
As intelligence requires knowledge, it is necessary for
the computers to acquire knowledge.
What is machine learning?
6. Training is the process of making the system able to
learn.
No free lunch rule:
Training set and testing set come from the same distribution
Need to make some assumptions or bias
Training and testing
7. There are several factors affecting the performance:
Types of training provided
The form and extent of any initial background knowledge
The type of feedback provided
The learning algorithms used
Two important factors:
Modeling
Optimization
Performance
8. The success of machine learning system also depends
on the algorithms.
The algorithms control the search to find and build the
knowledge structures.
The learning algorithms should extract useful information
from training examples.
Algorithms
9. Supervised learning ( )
Prediction
Classification (discrete labels), Regression (real values)
Unsupervised learning ( )
Clustering
Probability distribution estimation
Finding association (in features)
Dimension reduction
Semi-supervised learning
Reinforcement learning
Decision making (robot, chess machine)
Algorithms
13. Supervised: Low E-out or maximize probabilistic terms
Unsupervised: Minimum quantization error, Minimum
distance, MAP, MLE(maximum likelihood estimation)
What are we seeking?
E-in: for training
set
E-out: for testing
set
19. Support vector machine (SVM):
Linear to nonlinear: Feature transform and kernel function
Learning techniques
• Non-linear case
20. Unsupervised learning categories and techniques
Clustering
K-means clustering
Spectral clustering
Density Estimation
Gaussian mixture model (GMM)
Graphical models
Dimensionality reduction
Principal component analysis (PCA)
Factor analysis
Learning techniques
21. Face detection
Object detection and recognition
Image segmentation
Multimedia event detection
Economical and commercial usage
Applications
22. We have a simple overview of some
techniques and algorithms in machine
learning. Furthermore, there are more and
more techniques apply machine learning as
a solution. In the future, machine learning
will play an important role in our daily life.
Conclusion
23. [1] W. L. Chao, J. J. Ding, “Integrated
Machine Learning Algorithms for Human
Age Estimation”, NTU, 2011.
Reference