Machine Learning for Finance and Economics

Université Panthéon-Assas, Paris II

Instructor: Amir Sani (reachme@amirsani.com)

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Course Details

This is an applied course in Machine Learning intended for students of Economics and Finance. Course contents will be posted before each class.

Programming Language:

The official language of this course is Python 3. We will use Anaconda in class.

You are expected to know how to program in Python before taking this class.

Grading:

Grades will be based on individual quizzes and a team project.

Materials

The following materials are useful for the course:
- The Elements of Statistical Learning: Data Mining, Inference and Prediction, Python Notebooks
- Applied Predictive Modeling
- Probability Theory Review
- Linear Algebra Review

Course Schedule

Class 1: Foundation

• Slides
• Information Sheet
• Introduction to the Kaggle House Price Prediction Challenge
• Team Selection
• Review the algorithms in Scikit-Learn

Class Notebooks:

• Machine Learning Foundations
• Python Practice
• Machine Learning Exploration
• IMAGE FILES

Quizzes:

• Class 1 Quiz
• Lab 1 Quiz (I've merged all the lab quizzes into 1.)

Read:

• An overview of gradient descent optimization algorithms
• Visualizing MNIST
• Ch. 19: Deviations from Lehman E, Leighton T, Meyer AR. Mathematics for computer science. Technical report, 2006. Lecture notes; 2010.

For a more theoretical treatment, see:

• Statistical Learning Theory Lecture Notes, Percy Liang (I highly recommend these notes if you are interested in learning the theory behind machine learning.)
• Statistical Learning Theory Lecture Notes, Rui Castro

Class 2: Feature Selection

• A few useful things to know about machine learning
• Class 2 Quiz

Class Notebook:

• Feature Selection and Dimensionality Reduction

Read:

• Princial Component Analysis Explained Visually
• Selecting good features Part I through IV
• Does your Data have a Signal?

Class 3: Algorithms and Analysis

By the end of this class, you should have setup a basic submission for the Kaggle challenge.

• Class 3 Quiz
• First Kaggle team submission due.
• Review the algorithms in Scikit-Learn

Class Notebook:

• Regression and Expost analysis

Read:

• Gradient Boosting explained

Class 4: Model Selection and Evaluation

• Class 4 Quiz

Class Notebook:

• Model Evaluation

Read:

• Kaggle Ensembling Guide
• The Dangers of Overfitting or How to Drop 50 spots in 1 minute
• A survey of cross-validation procedures for model selection
• Residual diagnostics
• Residual analysis to determine the optimal cutoff frequency
• Hacking the Random Walk Hypothesis

Class 5: Final Submissions

• Quiz 5
• Final Kaggle submission due on Kaggle and as Python Notebook (this is your team project) at 10h00
• Kaggle competition (class) leaderboard review at 11h00

Please submit your final project notebook via email (to reachme@amirsani.com) and complete this Final Project submission form.