Discusses the benefits of AI and machine learning for hedge funds showcasing how these technologies are transforming the way some asset managers approach trading and operations.
Definition of classification
Basic principles of classification
Typical
How Does Classification Works?
Difference between Classification & Prediction.
Machine learning techniques
Decision Trees
k-Nearest Neighbors
In the world of recommendation systems, there are various theories and algorithms that work together to give the best results. Among these, the core recommendation algorithm is crucial. This paper will provide an introduction to some fundamental algorithms used in recommendation systems. These algorithms are like building blocks that help make recommendations more effective.
In this slide we have coverd the following topices
What is Artificial Intelligence?
What is Machine Learning?
Relationship among AI, ML and DL.
Human Brain Learning Process
Learning Vs Recognition
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition of Reinforcement Learning
Reinforcement Learning Application: AWS Deep racer
Markov Decision Process
Understanding Q-Learning Algorithm
Q-Learning Algorithm Example
Semi-Supervised Insight Generation from Petabyte Scale Text DataTech Triveni
Existing state-of-the-art supervised methods in Machine Learning require large amounts of annotated data to achieve good performance and generalization. However, manually constructing such a training data set with sentiment labels is a labor-intensive and time-consuming task. With the proliferation of data acquisition in domains such as images, text and video, the rate at which we acquire data is greater than the rate at which we can label them. Techniques that reduce the amount of labeled data needed to achieve competitive accuracies are of paramount importance for deploying scalable, data-driven, real-world solutions.
At Envestnet | Yodlee, we have deployed several advanced state-of-the-art Machine Learning solutions that process millions of data points on a daily basis with very stringent service level commitments. A key aspect of our Natural Language Processing solutions is Semi-supervised learning (SSL): A family of methods that also make use of unlabelled data for training – typically a small amount of labeled data with a large amount of unlabelled data. Pure supervised solutions fail to exploit the rich syntactic structure of the unlabelled data to improve decision boundaries. There is an abundance of published work in the field - but few papers have succeeded in showing significantly better results than state-of-the-art supervised learning. Often, methods have simplifying assumptions that fail to transfer to real-world scenarios. There is a lack of practical guidelines for deploying effective SSL solutions. We attempt to bridge that gap by sharing our learning from successful SSL models deployed in production
A study of Machine Learning approach for Predictive Maintenance in Industry 4.0Mohsen Sadok
I am delighted to share with you my graduation project presentation submitted for the award of Bachelor degree in #electromechanical_engineering.
#subject : A study of machine Learning approach for predictive maintenance in industry 4.0
>> the aim of the project is to Build and Develop Machine Learning models to predict Time-To-Failure (TTF) or Remaining Useful Life (RUL) of in-service equipment in order to pre-emptively trigger a maintenance visit to avoid adverse machine performance and minimizing the number and cost of unscheduled machine failures.
Technologies used: #Python, #TensorFlow, #Keras, #Sklearn, #RNN_LSTM, #XGboost, #LightGBM, #CATboost, #KNN, #SVM, #GaussianNB
>>> The Global Predictive Maintenance Market size is expected to reach $12.7 billion by 2025, rising at a market growth of 28.4% CAGR during the forecast period
#July_2019
#machine_learning
#deep_learning
#predictive_maintenance
#industry_4.0
(confidential details not presented)
LinkedIN : https://www.linkedin.com/posts/mohsen-sadok-254b0a110_a-study-of-machine-learning-approach-for-activity-6550815214206627840-Pq3G
Definition of classification
Basic principles of classification
Typical
How Does Classification Works?
Difference between Classification & Prediction.
Machine learning techniques
Decision Trees
k-Nearest Neighbors
In the world of recommendation systems, there are various theories and algorithms that work together to give the best results. Among these, the core recommendation algorithm is crucial. This paper will provide an introduction to some fundamental algorithms used in recommendation systems. These algorithms are like building blocks that help make recommendations more effective.
In this slide we have coverd the following topices
What is Artificial Intelligence?
What is Machine Learning?
Relationship among AI, ML and DL.
Human Brain Learning Process
Learning Vs Recognition
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition of Reinforcement Learning
Reinforcement Learning Application: AWS Deep racer
Markov Decision Process
Understanding Q-Learning Algorithm
Q-Learning Algorithm Example
Semi-Supervised Insight Generation from Petabyte Scale Text DataTech Triveni
Existing state-of-the-art supervised methods in Machine Learning require large amounts of annotated data to achieve good performance and generalization. However, manually constructing such a training data set with sentiment labels is a labor-intensive and time-consuming task. With the proliferation of data acquisition in domains such as images, text and video, the rate at which we acquire data is greater than the rate at which we can label them. Techniques that reduce the amount of labeled data needed to achieve competitive accuracies are of paramount importance for deploying scalable, data-driven, real-world solutions.
At Envestnet | Yodlee, we have deployed several advanced state-of-the-art Machine Learning solutions that process millions of data points on a daily basis with very stringent service level commitments. A key aspect of our Natural Language Processing solutions is Semi-supervised learning (SSL): A family of methods that also make use of unlabelled data for training – typically a small amount of labeled data with a large amount of unlabelled data. Pure supervised solutions fail to exploit the rich syntactic structure of the unlabelled data to improve decision boundaries. There is an abundance of published work in the field - but few papers have succeeded in showing significantly better results than state-of-the-art supervised learning. Often, methods have simplifying assumptions that fail to transfer to real-world scenarios. There is a lack of practical guidelines for deploying effective SSL solutions. We attempt to bridge that gap by sharing our learning from successful SSL models deployed in production
A study of Machine Learning approach for Predictive Maintenance in Industry 4.0Mohsen Sadok
I am delighted to share with you my graduation project presentation submitted for the award of Bachelor degree in #electromechanical_engineering.
#subject : A study of machine Learning approach for predictive maintenance in industry 4.0
>> the aim of the project is to Build and Develop Machine Learning models to predict Time-To-Failure (TTF) or Remaining Useful Life (RUL) of in-service equipment in order to pre-emptively trigger a maintenance visit to avoid adverse machine performance and minimizing the number and cost of unscheduled machine failures.
Technologies used: #Python, #TensorFlow, #Keras, #Sklearn, #RNN_LSTM, #XGboost, #LightGBM, #CATboost, #KNN, #SVM, #GaussianNB
>>> The Global Predictive Maintenance Market size is expected to reach $12.7 billion by 2025, rising at a market growth of 28.4% CAGR during the forecast period
#July_2019
#machine_learning
#deep_learning
#predictive_maintenance
#industry_4.0
(confidential details not presented)
LinkedIN : https://www.linkedin.com/posts/mohsen-sadok-254b0a110_a-study-of-machine-learning-approach-for-activity-6550815214206627840-Pq3G
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Presentazione Tesi Laurea Triennale in InformaticaLuca Marignati
Università degli Studi di Torino
Dipartimento di Informatica
Titolo: Apprendimento per Rinforzo e Applicazione ai Problemi di Pianificazione del Percorso
Topic: Machine Learning
Interpretability and Reproducibility in Production Machine Learning Applicat...Swaminathan Sundararaman
The past decade has seen tremendous growth in production deployments of machine learning algorithms across a range of applications such as targeted advertising, self driving cars, speech translation, medical diagnosis etc [1]. In these contexts, models make key decisions such as predicting the likelihood of a person committing a future crime, trustworthiness for a loan approval, medical diagnosis etc [2]. Presence of bias based on gender, geographical location, race etc., and their consequent negative impact, have been uncovered in several of these deployments [3], [4]. Industries and governments are reacting, enacting regulations requiring that decisions made by machine learning models be Interpretable/Explainable [5].
Explainability across the full range of ML and DL algorithms is an unsolved research problem, with many innovations over the last several years and entire conferences devoted to the topic. However, even simple explainability solutions that are considered established in development (training environments) run into additional difficulties when put into live production.
Our design pattern uses a well known technique for explainability - the Canary model (sometimes called Surrogate model) [6,7]. In this approach, a classically non-explainable technique, such as a Neural Network, is paired with an explainable model (that approximates the predictions of the non-explainable technique) such as a Decision Tree. As long as predictions match - the Canary model’s behavior can be used to provide a human understandable reasoning for the prediction.
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
This article introduces the concept of reinforcement learning (RL) and its application in building intelligent agents. It covers key RL techniques such as Markov Decision Processes, Q-Learning, Deep Q-Networks, and Policy Gradient Methods. The article also highlights the role of RL in data science, including data analysis, predictive modeling, and recommendation systems.
How to use Big Data and Machine Learning for attacks - specifically to achieve large scale attack planning and automatic attack execution.
This talk was given at Infiltrate 2015.
Lean Six Sigma Accelerating Cost Transformation SSCX Event Slides (2010)guest48fa5f
Slides from latest SSCX's Event "Lean Six Sigma: Accelerating Cost Transformation". Event was attended by more than 30 companies, representing service and manufacturing. Topic discussed include Lean, Six Sigma, Lean Six Sigma, Cost Transformation, bringing the case of Toyota, Garuda, JAL, Pharmacy Manufacturer, etc.
Deep reinforcement learning framework for autonomous drivingGopikaGopinath5
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is possible to propose a framework for autonomous driving using deep reinforcement learning.
It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Presentazione Tesi Laurea Triennale in InformaticaLuca Marignati
Università degli Studi di Torino
Dipartimento di Informatica
Titolo: Apprendimento per Rinforzo e Applicazione ai Problemi di Pianificazione del Percorso
Topic: Machine Learning
Interpretability and Reproducibility in Production Machine Learning Applicat...Swaminathan Sundararaman
The past decade has seen tremendous growth in production deployments of machine learning algorithms across a range of applications such as targeted advertising, self driving cars, speech translation, medical diagnosis etc [1]. In these contexts, models make key decisions such as predicting the likelihood of a person committing a future crime, trustworthiness for a loan approval, medical diagnosis etc [2]. Presence of bias based on gender, geographical location, race etc., and their consequent negative impact, have been uncovered in several of these deployments [3], [4]. Industries and governments are reacting, enacting regulations requiring that decisions made by machine learning models be Interpretable/Explainable [5].
Explainability across the full range of ML and DL algorithms is an unsolved research problem, with many innovations over the last several years and entire conferences devoted to the topic. However, even simple explainability solutions that are considered established in development (training environments) run into additional difficulties when put into live production.
Our design pattern uses a well known technique for explainability - the Canary model (sometimes called Surrogate model) [6,7]. In this approach, a classically non-explainable technique, such as a Neural Network, is paired with an explainable model (that approximates the predictions of the non-explainable technique) such as a Decision Tree. As long as predictions match - the Canary model’s behavior can be used to provide a human understandable reasoning for the prediction.
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
This article introduces the concept of reinforcement learning (RL) and its application in building intelligent agents. It covers key RL techniques such as Markov Decision Processes, Q-Learning, Deep Q-Networks, and Policy Gradient Methods. The article also highlights the role of RL in data science, including data analysis, predictive modeling, and recommendation systems.
How to use Big Data and Machine Learning for attacks - specifically to achieve large scale attack planning and automatic attack execution.
This talk was given at Infiltrate 2015.
Lean Six Sigma Accelerating Cost Transformation SSCX Event Slides (2010)guest48fa5f
Slides from latest SSCX's Event "Lean Six Sigma: Accelerating Cost Transformation". Event was attended by more than 30 companies, representing service and manufacturing. Topic discussed include Lean, Six Sigma, Lean Six Sigma, Cost Transformation, bringing the case of Toyota, Garuda, JAL, Pharmacy Manufacturer, etc.
Deep reinforcement learning framework for autonomous drivingGopikaGopinath5
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is possible to propose a framework for autonomous driving using deep reinforcement learning.
It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios.
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
Yes of course, you can easily start mining pi network coin today and sell to legit pi vendors in the United States.
Here the what'sapp contact of my personal vendor.
+12349014282
#pi network #pi coins #legit #passive income
#US
^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Duba...mayaclinic18
Whatsapp (+971581248768) Buy Abortion Pills In Dubai/ Qatar/Kuwait/Doha/Abu Dhabi/Alain/RAK City/Satwa/Al Ain/Abortion Pills For Sale In Qatar, Doha. Abu az Zuluf. Abu Thaylah. Ad Dawhah al Jadidah. Al Arish, Al Bida ash Sharqiyah, Al Ghanim, Al Ghuwariyah, Qatari, Abu Dhabi, Dubai.. WHATSAPP +971)581248768 Abortion Pills / Cytotec Tablets Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujeira, Ras Al Khaima, Umm Al Quwain., UAE, buy cytotec in Dubai– Where I can buy abortion pills in Dubai,+971582071918where I can buy abortion pills in Abudhabi +971)581248768 , where I can buy abortion pills in Sharjah,+97158207191 8where I can buy abortion pills in Ajman, +971)581248768 where I can buy abortion pills in Umm al Quwain +971)581248768 , where I can buy abortion pills in Fujairah +971)581248768 , where I can buy abortion pills in Ras al Khaimah +971)581248768 , where I can buy abortion pills in Alain+971)581248768 , where I can buy abortion pills in UAE +971)581248768 we are providing cytotec 200mg abortion pill in dubai, uae.Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman Fujairah Ras Al Khaimah%^^%$Zone1:+971)581248768’][* Legit & Safe #Abortion #Pills #For #Sale In #Dubai Abu Dhabi Sharjah Deira Ajman
BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just what'sapp this number below. I sold about 3000 pi coins to him and he paid me immediately.
+12349014282
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
Seminar: Gender Board Diversity through Ownership Networks
Cognitive Cloud: Trading and Artificial Intelligence Workshop
1. COGNITIVE CLOUD
TRADING AND ARTIFICIAL INTELLIGENCE WORKSHOP
変通 [hen-tsoo]
noun
1. Resourcefulness - the quality of being able to cope with a difficult situation
2. Adaptability -the ability to change (or be changed) to fit changed circumstances
3. Agility - the power of moving quickly and easily; nimbleness
2. ̶̶ Introduction to machine learning
Taxonomy
Application areas
Application to finance
̶̶ Demo of basic machine learning algorithms
Supervised learning: linear and kernel regression
Unsupervised learning: dimension reduction
̶̶ Advanced Demo
Factor selection
AGENDA
2
4. ̶̶ Supervised Learning
Find the connection between input data and their associated labels
̶̶ Unsupervised Learning
Discover the underlying structure given the input data
̶̶ Reinforcement Learning
Solving the problem of how to act for maximising rewards in an environment
TAXONOMY
4
5. ̶̶ Classic Problem Setting
Classification
Regression
Learning to rank
̶̶ Some Applications
Object recognition from images
Speech recognition
SUPERVISED LEARNING
5
6. ̶̶ Classic Problem Setting
Clustering
Dimension reduction
̶̶ Some Applications
Data visualisation
(Some) Recommendation system
UNSUPERVISED LEARNING
6