Machine Learning by Lokvamsi Anumukonda

Article Link-967

f2015963 — 2015-09-24 08:00:56 UTC

http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf

f2015963 — 2015-09-24 08:05:01 UTC

What is Machine Learning

f2015096 — 2015-09-24 08:07:00 UTC

https://www.youtube.com/watch?v=WXHM_i-fgGo

ID No.

f2015967 — 2015-09-25 08:01:11 UTC

Akash - 2015A7PS967H

Vishnu - 2015A7PS963H

Lokvamsi - 2015A7PS096H

Applications of Machine Learning in Finance

f2015967 — 2015-09-26 05:19:48 UTC

There are many applications of Machine Learning in finance. Machine learning can assist the financial industry in the evaluation of assets. Combination of models using machine learning can help predict liquidity with consistent success. U.S. market estimates today are able to be more liquid due to transparency and the accessibility of data which allows the application of machine learning to have a bigger impact. Stock marcket forcasting can also be done using Machine Learning by parsing news headlines. Machine Learning algorithms are efficient, generic and have an automated approach to process such data. The basic premise is that machines can be programmed by Google to conduct web searches or by Amazon and Netflix to recommend movies and books, so there’s no reason why they shouldn’t be able to be trained to make investment decisions. The field of knowledge in the area is also expanding at a rapid clip.

- Akash 967

What is Machine Learning?

f2015967 — 2015-10-07 08:28:39 UTC

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. - Akash 967

What are the applications of Machine Learning?

f2015967 — 2015-10-07 08:29:34 UTC

Machine Learning has a lot of applications. Some of them are protecting animals, predicting heart failures, and predicting strokes and seizures. - Akash 967

What is the future of Machine Learning?

f2015967 — 2015-10-07 09:07:35 UTC

Machine Learning has a very bright future. Some things Machine Learning can transform into are AI based Virtual Machines and Better Boltzmann Machines. - Akash 967

How did Machine Learning begin?

f2015967 — 2015-10-07 09:16:12 UTC

Machine learning grew out of the quest for artificial intelligence. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature in the 1990's. Akash 967

Article Link-096

f2015963 — 2015-10-07 15:57:40 UTC

http://alex.smola.org/drafts/thebook.pdf

Applications Of Machine Learning in Social Media

f2015096 — 2015-10-07 16:30:40 UTC

Applying the basic ideas of machine learning to social media or social networks is termed as Social Media Mining. Its the process of analyzing and taking information from various user inputs and recognizing patterns in the data. It uses some principle algorithms designed using inputs from various fields such as computer science, network science, statistics, and data mining. Using these tools, we can predict and measure user patterns from large scale social media data. With the advancement of technology, from what was a ‘one to many’ internet (few people uploading information which is accessible by many), now almost every single person can upload their data on various sites. For eg, social networks such as Facebook tend to receive hundreds of TB of data every single. Facebook now has a new “People you may know” section where it analyzes recent friend requests, places you’ve been to, and the people you talk to often and suggests people that you might know so you can socialize more. Twitter too has a similar algorithm which takes your recent tweets, hashtags, and follows into account and helps you find other ‘strangers’ which have the same interests as you. These interests can be as broad as #coffeelovers or even as specific like a particular author or movie you might like. Machine learning also plays a key role in preventing frauds in social networking sites. Profile information such as Name, Age, Location might be incomplete or even misleading (like Birthplace-Lolo Land) which can either be reported or corrected based on user inputs and usage.

-Lokvamsi 096

Article Link-963

f2015963 — 2015-10-07 16:33:00 UTC

https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

Applications of Machine Learning in E-commerce

f2015963 — 2015-10-07 16:33:48 UTC

Machine Learning algorithms set the basis of E-commerce,prominent in todays generation.A generalised requirement makes it difficult to stimulate machine learning.But in the case of E-commerce,the requirement is narrow-we need a system which helps give suggestions and advertisements to the customer based on the product bought.For example,if someone buys a kettle from flipkart,the machine learning algorithm is likely to offer products like green tea bags,which are complimentary to the product bought.This is how machine learning can help bring in more revenues,by suggesting products in context of buyers individually.The algorithms for this system can also be set to display contextual products of sellers willing to pay more to have their products suggested,which brings in more revenues.The possibilities are endless.Yet an important factor of machine learning is the ways in which it can adapt to change,a prominent role player in today's world,where the buyer's needs and preferences are constantly changing.

-Vishnu 963

Article Proof-963

f2015963 — 2015-10-07 17:12:30 UTC

-Pedro Domingos,Department of Computer Science and Engineering,University of Washington.

-Published in:Washington,USA.

-Year of publication:2013

-References used:

1.E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36:105–142, 1999.

2.https://en.wikipedia.org/wiki/E-commerce

3.https://www.quora.com/What-are-the-applications-of-Machine-learning-in-E-commerce-industry 4.https://ceasefiremagazine.co.uk/ibm-jeopardy/

5.J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. Byers. Big data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute, 2011.

Article Proof - 967

f2015967 — 2015-10-07 17:15:19 UTC

Name of Author: Tom M. Mitchell

City of Publication: Pittsburgh

Year of Publication: July 2006

Qualifications: B.Sc from M.I.T

Ph.D from Harvard

Reference:

1. https://en.wikipedia.org/wiki/Machine_learning

2. https://www.youtube.com/watch?v=yDLKJtOVx5c

3. http://www-stat.wharton.upenn.edu/~steele/Courses/9xx/Resources/MLFinancialApplications/MLFinance.html

4. http://www.bloomberg.com/company/announcements/workshop-applying-machine-learning-financial-sector/

5. https://divergence.academy/business-models/applications-of-machine-learning-in-finance/

Article Proof-096

f2015096 — 2015-10-07 17:19:55 UTC

Name of Author: Alexander J. Smola

City of Publication: Cambridge, UK

Year of Publication: 2008

Professor, Carnegie Mellon University and CEO, Marianas Labs

References used-

1. http://machine-learning.blogspot.in/

2.A Machine Learning Approach to Twitter User Classification -Marco Pennacchiotti and Ana-Maria Popescu

3.A Machine Learning Based Approach for Predicting

Undisclosed Attributes in Social Networks- Gergely Kotyuk, Laboratory of Cryptography and Systems Security (CrySyS)

4.Introduction to Machine Learning-Alex Smola and S.V.N. Vishwanathan

5.https://www.coursera.org/learn/machine-learning

-Lokvamsi

Drawbacks of Machine Learning

f2015963 — 2015-10-07 17:30:05 UTC

Although helpful,further advancements in machine learning could potentially result in employment opportunities being replaced by machine learning mechanisms,causing unemployment.For all we know,machine learning could even result in machines funtioning beyond the laws that govern them,which could be catastrophic on many levels.

-Vishnu 963

f2015096 — 2015-10-07 17:30:25 UTC

f2015096 — 2015-10-07 17:33:15 UTC

f2015096 — 2015-10-07 17:37:37 UTC

Machine Learning-Stanford lecture

f2015963 — 2015-10-07 17:41:07 UTC

https://www.youtube.com/watch?v=UzxYlbK2c7E

Survey Questions-Group

f2015096 — 2015-10-07 18:07:45 UTC

1. How many times have you actually used the suggestions boxes that come up at the sides of websites you use?

2. If youre starting a new buisiness requiring a factory, would you trust a machine learning algorithm to run the factory or would you prefer hiring someone to do the same job?

3. What do you think of the scope of research via machine learning?

4. Do you think machine learning could a potential reason for artificial intelligence to surpass human intelligence

5. How do you think the concept of machine learning can impact your daily life?

New Terms 967

f2015967 — 2015-10-07 18:10:08 UTC

Autonomous - Having the freedom to act independently

Temporal - Relating to time

Synergy - The interaction or cooperation of two or more organizations, substances, or other agents to produce a combined effect greater than the sum of their separate effects

Pareto-optimal - A state of allocation of resources in which it is impossible to make any one individual better off without making at least one individual worse off

Niche - A comfortable or suitable position in life or employment.

New Terms 963

f2015967 — 2015-10-07 18:26:08 UTC

Boolean - Either true or false

Embody - be an expression of or give a tangible or visible form to

Bugbear - a cause of obsessive fear, anxiety, or irritation

Panacea - a solution or remedy for all difficulties or diseases

Disjunction - a lack of correspondence or consistency

New Terms 096

f2015096 — 2015-10-07 18:27:27 UTC

Raft- A great number, or amount

Transduction- The process by which DNA is transferred from one bacterium to another by a virus.

Overarching- Comprehensive or all-embracing

Arduous-Involving or requiring strenuous effort

Amenable-Open and responsive to suggestion or capable of being acted upon in a particular way

Survey Questions(Justification)

f2015963 — 2015-10-07 18:29:04 UTC

1.This helps us understand how effective machine learning concepts are

2.This gives an insight on how much people trust machine learning as of now.

3.This give us opinions on what people think of the extent to which machine learning can help in a field as important as research

4.This gives information on how smart people think machines can be via machine learning.

5.This will help us understand the potential impact of machine learning on an individual level.

f2015096 — 2015-10-07 19:06:02 UTC