CS计算机代考程序代写 deep learning flex finance algorithm PowerPoint 演示文稿

PowerPoint 演示文稿

MFIN 290 Application of Machine Learning in
Finance: Lecture 6

Yujie He

7/31/2021

Agenda

Recap of last lecture (Unsupervised Learning and Neural Network)

Brief Introduction to Deep Learning

Lab: Auto-encoder for Fraud Detection Cont’d

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Last Lecture

Unsupervised Learning

Dimension Reduction

Overview of different approach families, PCA, SVD

Clustering

Common methods

Evaluation

Real world example use case

Neural Network

Activation function

Loss function

Back-propagation

Gradient descent

Regularization

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Brief Introduction to Deep Learning

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What is Deep learning

Deep learning is a subfield of machine learning

Most machine learning methods work well because of

human-designed representations and input features

For example: features for finding named entities like

locations or organization names (Finkel et al., 2010):

Machine learning becomes just optimizing weights to

best make a final prediction

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Feature learning example

LSTM based

language model

LSTM cell

activation

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Deep Learning (DNN) vs. Neural Network

Neural networks with MANY layers!

Vanishing/exploding gradient

clipping, better activation functions, better optimizers

Structural change, residual/highway network

A variety of different model architectures

Convolution neural network

Transformer (multi-head attention)

Generative Adversarial Network (GAN)

Key differentiator is representation learning

Need large amount of training data

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Why do we explore deep learning

Manually designed features are often over-specified, task specific, incomplete and take many efforts to

design and validate

Learned Features are easy to adapt, fast to learn

Deep learning provides a very flexible, (almost?) universal, learnable framework for representing world,

visual and linguistic information.

Deep learning can learn unsupervised (from raw text) and supervised (with specific labels like

positive/negative)

Transfer learning

Low resource languages/tasks

Zero-shot/few-shot learning

Cross-lingual applications

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Why do we explore deep learning

In ~2010 deep learning techniques started outperforming other machine learning techniques. Why this

decade?

Large amounts of training data favor deep learning

Faster machines and multicore CPU/GPUs favor Deep Learning

New/larger models, algorithms, ideas

Better, more flexible learning of intermediate representations

Effective end-to-end joint training system

Improved model structure to encode more information (e.g. transformers)

Larger models with higher capacity (e.g. 17B parameters)

Better regularization (e.g. dropouts, batch norm) and optimization methods (e.g. Adam)

Improved performance (initially in speech and vision, then NLP)

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Best Practices

Often does not hurt to normalize data (e.g. has big impact on KNN with Euclidean distance)

PCA

SVD

KNN

LASSO/Ridge regression (penalty is coefficient magnitude dependent)

Feature importance interpretation (e.g. regression model)

Tree-based models are not sensitive to magnitude of variables

Build a benchmark model

Find a good metric that aligns with business goal

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Lab: Auto-encoder for Fraud Detection

Colab

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https://colab.research.google.com/drive/1q_AuysUon2QB8V55MdzXoXHqPpqZ55WR?usp=sharing

Next Step

Today is deadline of submitting group member list and project selection

Lecture 7: Prof. Edward Sheng will cover times series, Kalman filter, and state-space model

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