Examples and code for the Practical Machine Learning workshop series

Overview

Practical Machine Learning Workshop Series

Practical Machine Learning for Quantitative Finance

  • Post conference workshop at the WBS Spring Conference
  • Date: Monday 29th & Tuesday 30th March 2021
  • Time: EDT: 9-11am / BST: 2-4pm / CEST: 3-5pm

Agenda

We will focus on two concepts in machine learning that hold great promise for quant finance:

  • Regression models on Day 1
  • Generative models on Day 2

Each day, we will discuss theory during the first hour, and then use what we learned to solve practical quant finance problems during the second hour.

During the practice sessions, we will review, update and run code on Colab and AWS. Participants are welcome to just observe, or if desired also participate in writing or running the code (participation is strictly optional). The code will be available on GitHub after the workshop.

Day 1 – Regression Models

  • Regression model types
  • Feedforward networks
  • Autoencoders

Practice session: Estimation of Drift in Real-World Measure

Day 2 – Generative Models

  • Generative model types
  • Joint and Conditional Probabilities
  • Gibbs sampling

Practice session: Estimation of Probability in Real-World Measure

MegFlow - Efficient ML solutions for long-tailed demands.

Efficient ML solutions for long-tailed demands.

旷视天元 MegEngine 371 Dec 21, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022
💀mummify: a version control tool for machine learning

mummify is a version control tool for machine learning. It's simple, fast, and designed for model prototyping.

Max Humber 43 Jul 09, 2022
Drug prediction

I have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Dr

Khazar 1 Jan 28, 2022
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022
Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

Jeong-Yoon Lee 720 Dec 25, 2022
TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models

538 Jan 01, 2023
Data Efficient Decision Making

Data Efficient Decision Making

Microsoft 197 Jan 06, 2023
Machine Learning Algorithms

Machine-Learning-Algorithms In this project, the dataset was created through a survey opened on Google forms. The purpose of the form is to find the p

Göktuğ Ayar 3 Aug 10, 2022
Responsible Machine Learning with Python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

ph_ 624 Jan 06, 2023
inding a method to objectively quantify skill versus chance in games, using reinforcement learning

Skill-vs-chance-games-analysis - Finding a method to objectively quantify skill versus chance in games, using reinforcement learning

Marcus Chiam 4 Nov 19, 2022
Generate music from midi files using BPE and markov model

Generate music from midi files using BPE and markov model

Aditya Khadilkar 37 Oct 24, 2022
SynapseML - an open source library to simplify the creation of scalable machine learning pipelines

Synapse Machine Learning SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. Sy

Microsoft 3.9k Dec 30, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 09, 2023
Traingenerator 🧙 A web app to generate template code for machine learning ✨

Traingenerator 🧙 A web app to generate template code for machine learning ✨ 🎉 Traingenerator is now live! 🎉

Johannes Rieke 1.2k Jan 07, 2023
Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Diabetes This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type

1 Mar 28, 2022
Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Thines Kumar 1 Jan 31, 2022
Repositório para o #alurachallengedatascience1

1° Challenge de Dados - Alura A Alura Voz é uma empresa de telecomunicação que nos contratou para atuar como cientistas de dados na equipe de vendas.

Sthe Monica 16 Nov 10, 2022
Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn.

Repository Status for Scikit-learn Live webpage Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn. Running local

Thomas J. Fan 6 Dec 27, 2022
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 2022