About this course: 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, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. 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.
Who is this class for: The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance
Created by: New York University Tandon School of Engineering
Taught by: Igor Halperin
| Basic Info | Course 1 of 4 in the Machine Learning and Reinforcement Learning in Finance Specialization |
| Level | Beginner |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Artificial Intelligence & Machine Learning
11 videos, 3 readings
- Video: Specialization Objectives
- Video: Specialization Prerequisites
- Video: Artificial Intelligence and Machine Learning, Part I
- Video: Artificial Intelligence and Machine Learning, Part II
- Video: Machine Learning as a Foundation of Artificial Intelligence, Part I
- Video: Machine Learning as a Foundation of Artificial Intelligence, Part II
- Video: Machine Learning as a Foundation of Artificial Intelligence, Part III
- Video: Machine Learning in Finance vs Machine Learning in Tech, Part I
- Video: Machine Learning in Finance vs Machine Learning in Tech, Part II
- Video: Machine Learning in Finance vs Machine Learning in Tech, Part III
- Reading: The Business of Artificial Intelligence
- Reading: How AI and Automation Will Shape Finance in the Future
- Reading: A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1
- Notebook: Euclidean Distance Calculation
Graded: Module 1 Quiz
Graded: Euclidean Distance Calculation
WEEK 2
Mathematical Foundations of Machine Learning
6 videos, 2 readings
- Video: The No Free Lunch Theorem
- Video: Overfitting and Model Capacity
- Video: Linear Regression
- Video: Regularization, Validation Set, and Hyper-parameters
- Video: Overview of the Supervised Machine Learning in Finance
- Reading: I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
- Reading: Leo Breiman, “Statistical Modeling: The Two Cultures”
- Notebook: Linear Regression
Graded: Module 2 Quiz
Graded: Linear Regression
WEEK 3
Introduction to Supervised Learning
7 videos, 3 readings
- Video: A First Demo of TensorFlow
- Video: Linear Regression in TensorFlow
- Video: Neural Networks
- Video: Gradient Descent Optimization
- Video: Gradient Descent for Neural Networks
- Video: Stochastic Gradient Descent
- Reading: A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)
- Reading: E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.
- Reading: J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-41
- Notebook: Tobit Regression
Graded: Module 3 Quiz
Graded: Tobit Regression
WEEK 4
Supervised Learning in Finance
9 videos, 2 readings
- Video: Fundamental Analysis
- Video: Machine Learning as Model Estimation
- Video: Maximum Likelihood Estimation
- Video: Probabilistic Classification Models
- Video: Logistic Regression for Modeling Bank Failures, Part I
- Video: Logistic Regression for Modeling Bank Failures, Part II
- Video: Logistic Regression for Modeling Bank Failures, Part III
- Video: Supervised Learning: Conclusion
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
- Reading: A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)
- Notebook: Course Project
Graded: Module 4 Quiz
Graded: Course Project
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New York University Tandon School of Engineering
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
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| Access to course materials | Available |
| Access to graded materials | Available |
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