fintech exam

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error rate formula

(FP+FN)/(TP+TN+FP+FN)

accuracy formula

(TP + TN)/(TP + TN+ FP + FN)

convolutional neural networks

- good for high dimensional unstructured data - consist of convolutional and pooling layers, as well as fully connected layers -convolution/pooling layers perform automatic feature extraction -fully connected layers then use those features as inputs

hopfield network

backfed input cell

deep belief networks

energy based models app example: recommendation systems

Actor-critic

example of reinforcement learning algorithm

Dynamic programming

example of reinforcement learning algorithm

Policy gradient

example of reinforcement learning algorithm

mean squared erros(MSE)

metrics for unsupervised learning

Denoising autoencoder

noisy input cell- probabilistic hiddent cell - match input output

ai application in asset management

risk management

ai application in asset management

robo-advice

ai application in insurance

robo-advice

volume

scale of data

deep learning applications

sef driving car entertainment visual recognition virtual assistant fraud detection natural language processin g news aggregation detecting development delay in children colourisation of black and white images adding sounds to silent mvies healthcsare personalisations automatic machine transaltion automatic handwriting generaiton demographic and electric predictions automatic game playing language transaltions pixel restoration ohoto desctiptions deep dreaming

ai application in retail and corporate banking

service, credit underwriting and scoring

generative adversarial networks

similar to variational autoencoders, used to learn underlying data distributions and generate new data two networks : generator an discriminator compete in a policeman bandit adversarial game generators create content discriminators clarrify them as fake or authentic purpose: to train the generator to create content as close to authentic as possible

velocity

speed of data generation, procession, and analysis

most common types of neural networks

1. convolutional 2.recurrent NN 3. autoencoders 4.generative adversarial networks

how supervised machine learning works

1. provide the machine learning algorithm categorized or labeled input 2. feed the machine new, unlabeled information to see if it tags new data appropriately. if not, continue refining the algorithm

categorisation of learning algorithms

1. supervised learning 2.unsupervised learning 3. reinforcement learning

Areas in finance most affected by advancements in AI are

1.Asset management 2.Algorithmic trading 3.Blockchain-based applications

lifecycle of AI systems

1.Planning and design, data accumulation and preprocessing, model building 2.Verification and validation 3.Deployment, operation, maintenance

false positive rate formula

FP/(FP+TN)

metrics

Measurements that evaluate algorithms

negative predictive value formula

TN/(TN+FN)

specificity formula

TN/(TN+FP)

Sensitivity formula

TP/(TP+FN)

precision formula

TP/(TP+FP)

reinforcement learning

algorithms (agents) learn a policy that maps states to actions through the optimization of a cumulative return based on trial and error interactions between the agents and their environemnt. input can be labeled or unlabeled

DEEP BELIEF NETWORK (DBN)

backfed input -probabilistic hidden cell- hidden - probabilistic hidden - hiden - probabilistic hidden - match input output cell

Boltzmann Machine

backfed input cell - probabilistic hidden cell

restricted BM

backfed input cell - probabilistic hidden cell

veracity

certainly, reliability

ai application in retail

chat boxes for clients

ai application in insurance

claims management

Supervised Learning types

classification and regression

unsupervised learning types

clustering and dimensionality reduction

ai application in corporate banking

credit loss forecasting, fraud monitoring and detection

ai application in retail and corporate banking

customer service

Q-learning

example of reinforcement learning algorithm

Boosting algorithm

example of supervised learning

decision trees/random forests

example of supervised learning

linear/logistic regression

example of supervised learning

support vector machines

example of supervised learning

Hierarchical aggregating clustering

example of unsupervised learning

K-means

example of unsupervised learning

gaussian mixture models

example of unsupervised learning

Big Data

explosion of quantity of potentially relevant data

Supervised Learning

given data are labeled, ML aims to learn an unknown function that maps features to labels

unsupervised learning

given data are unlabeled, aims to extract similarity patterns

reinforcement learning

goal-directed optimization, software agents interact with their environment aiming to maximize some notion of reward

recurrent neural networks

good for time series input and output data

variety

heterogeneous(images, categorical, real-valued, binary)

Gated Recurrent Unit (GRU)

input - different memory cell- different memory cell - output

kohonen network

input - hidden

Auto encoder

input - hidden - match input output cell

Sparse Autoencoder

input - hidden - match input output cell

SVM (Support Vector Machines)

input - hidden - output

deep residual network

input - hidden - output

NEURAL TURING MACHINE (NTM)

input - hidden - output and blue is match input output cell

deep feed forward

input - hidden cell - output

feed forward neural

input - hidden cell - output

radial basis network

input - hidden cell - output

DCN deep convolutional netowrk

input - kernel - convolutional or pool - hidden - output

Variational Auto Encoder

input - probabilistic hidden cell- match input output cell

recurrent neural networks

input - reccurent cell-recurrent cell - output

liquid state machine

input - spiking hidden cell - output

unsupervised learning

input data are unlabeled: only features

deconvolutional network

input- convolution or pool- kernel - output

Extreme Learning Machine/ELM

input- hidden - output

Generative Adversarial Networks

input- hiden - match input output cell- hiden - match input output cell

deep convolutional inverse graphics network

input- ketnel - convolution - probalistic hidden cell- convolution - karnel - output

LSTM (Long Short Term Memory)

input- memory cell- memory cell - output

ESN echo state network

input- recurrent cell - output

two main types of recurrent neural networks

long short term memory and gated recurrent units

artificial intelligence

machine based systems with varying levels of autonomy that can make predictions, recommendation, or decisions using data

ai application in asset management

management of portfolio strategies

AUC(area under the curve)

metrics for supervised learning

F1 score

metrics for supervised learning

ROC(receiver operating characteristic curve)

metrics for supervised learning

confusion matrix

metrics for supervised learning

precision/recall

metrics for supervised learning

sensitivity

metrics for supervised learning

MAE(Mean Absolute Error)

metrics for unsupervised learning

RMSE

metrics for unsupervised learning

graph neural networks

model the relationship between the nodes in a graph and produce a number representation of it

Deep Learning

neural networks that are implemented over multiple layers of feature extraction and learning

Markov Chain MC

probabilistic hidden cell

trained agent takes proper actions that optimize a cumulative future reward

rainforcement learning metrics

transformers

state of the art method for natural language processing and understanding

machine learning

subset of AI and consists of various algortihms that are able to learn from data

ai application in retail and corporate banking

tailored products

other types of deep NNs

transformer networks graph neural netwroks physics-informed bayesian

autoencoders

used for compessing high dimensional data to low dimensional representations able to learn latent data distribution, used to generate new data

4 Vs: characteristics of big data

volume velocity veracity variety

classification

when labels are binary/categorical. sorting items into categories

regression

when labels are real-valued (dollars, weights, etc_


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