Guided Tour of Machine Learning in Finance

Description

In this course, you will :

• This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance.
• Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course,
• The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. 

Syllabus :

1. Artificial Intelligence & Machine Learning

• Specialization Objectives
• Specialization Prerequisites
• Artificial Intelligence and Machine Learning
• Machine Learning as a Foundation of Artificial Intelligence

 

2. Mathematical Foundations of Machine Learning

• Generalization and a Bias-Variance Tradeoff
• The No Free Lunch Theorem
• Overfitting and Model Capacity
• Linear Regression
• Regularization, Validation Set, and Hyper-parameters
• Overview of the Supervised Machine Learning in Finance

 

3. Introduction to Supervised Learning

• DataFlow and TensorFlow
• A First Demo of TensorFlow
• Linear Regression in TensorFlow
• Neural Networks
• Gradient Descent Optimization
• Gradient Descent for Neural Networks
• Stochastic Gradient Descent

 

4. Supervised Learning in Finance

• Regression and Equity Analysis
• Fundamental Analysis
• Machine Learning as Model Estimation
• Maximum Likelihood Estimation
• Probabilistic Classification Models
• Logistic Regression for Modeling Bank Failures

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