FINTECH EXAM 2 QUESTIONS WITH DETAILED VERIFIED
AND 100% ACCURATE ANSWERS
Internet of Things (IoT) a world where interconnected, Internet-enabled
devices or "things" can collect and share data without human
intervention
AI and Machine Learning in Finance Used to assess credit, optimize
scarce capital, find signals for higher returns and optimize trading
execution, used by public and private sector for regulatory compliance,
surveillance, data quality assessment and fraud detection
Machine Learning Seeks to extract info from large amounts of data.
Goal is to automate decision-making processes by "learning" from
known examples to determine an underlying structure in the data.
Emphasis in on the ability to generate structure or predictions from data
without human help.
Deep Learning Relies on sophisticated algorithms to address highly
complex tasks, such as image classification, face recognition, speech
recognition, and natural language processing
Supervised Learning Uses algorithms that infer patterns between a set of
inputs and labeled output. The inferred pattern is then used to map a new
input set into a predicted output
Unsupervised Learning Does not use labeled data. Because the
algorithm is not trained with labeled output data, the algorithm seeks to
discover structure within the data themselves
, Applying Machine Learning to Investment Management Exploding
volume and diversity of data, as well as increasing economic value of
insights extracted from basic data, have made a basic understanding of
data science an important part of the tool kit for investment management
Artificial Intelligence Application of computational tools to address
tasks traditionally requiring human sophistication. Can be applied to the
problem of big data
Reinforced Learning Falls in between supervised and unsupervised
learning. Algorithm is fed unlabeled data set, chooses an action for each
data point, and receives feedback that helps the algorithm learn.
4 V's of Big Data Volume, Velocity, Veracity, Variety
Volume - Big Data Huge data size, terabytes - perabytes
Velocity - Big Data High speed of data flow, change and processing
Variety - Big Data Various Data Sources (Social, Mobile, M2M,
structured and unstructured data)
Veracity - Big Data Various levels of data uncertainty and reliability
AND 100% ACCURATE ANSWERS
Internet of Things (IoT) a world where interconnected, Internet-enabled
devices or "things" can collect and share data without human
intervention
AI and Machine Learning in Finance Used to assess credit, optimize
scarce capital, find signals for higher returns and optimize trading
execution, used by public and private sector for regulatory compliance,
surveillance, data quality assessment and fraud detection
Machine Learning Seeks to extract info from large amounts of data.
Goal is to automate decision-making processes by "learning" from
known examples to determine an underlying structure in the data.
Emphasis in on the ability to generate structure or predictions from data
without human help.
Deep Learning Relies on sophisticated algorithms to address highly
complex tasks, such as image classification, face recognition, speech
recognition, and natural language processing
Supervised Learning Uses algorithms that infer patterns between a set of
inputs and labeled output. The inferred pattern is then used to map a new
input set into a predicted output
Unsupervised Learning Does not use labeled data. Because the
algorithm is not trained with labeled output data, the algorithm seeks to
discover structure within the data themselves
, Applying Machine Learning to Investment Management Exploding
volume and diversity of data, as well as increasing economic value of
insights extracted from basic data, have made a basic understanding of
data science an important part of the tool kit for investment management
Artificial Intelligence Application of computational tools to address
tasks traditionally requiring human sophistication. Can be applied to the
problem of big data
Reinforced Learning Falls in between supervised and unsupervised
learning. Algorithm is fed unlabeled data set, chooses an action for each
data point, and receives feedback that helps the algorithm learn.
4 V's of Big Data Volume, Velocity, Veracity, Variety
Volume - Big Data Huge data size, terabytes - perabytes
Velocity - Big Data High speed of data flow, change and processing
Variety - Big Data Various Data Sources (Social, Mobile, M2M,
structured and unstructured data)
Veracity - Big Data Various levels of data uncertainty and reliability
