Optimizing AI Strategies

Overview

This course covers various aspects of improving AI models. Topics include introduction to model optimization, hyperparameter tuning, regularization techniques, evaluating and optimizing strategies, and deployment considerations.

Students will learn how to monitor, evaluate and enhance model performance, prevent overfitting, and apply techniques for real-world scenarios.

Syllabus

• Introduction to AI Model Optimization
We review how AI models work in principle and important terminology used in AI model training and optimization. We talk about where AI model optimization applies in using AI models for trading.
• Regularization Techniques to Prevent Overfitting
Overfitting is a common issue when training AI models for trading. We’ll explore bias, variance, and the role of hyperparameters in the context of various AI model types.
• Hyperparameter Tuning Methods
Get hands-on with AI model hyperparameters and discuss the various methods available to us for tuning them in a systematic or ad-hoc way, as well as the advantages and disadvantages of each method.
• Evaluating and Optimizing AI Strategies
We discuss some practical methods and important considerations related to model optimization and evaluation in the context of AI models for trading.
• Deployment and Real-World Considerations
We analyze important practical considerations for using AI models for trading. We discuss things we need to keep in mind as we maintain or iterate on our deployed models.
• Project: Building and Optimizing a Classification Model for Trading
Optimize a stock price prediction model using data preprocessing, hyperparameter tuning, over/underfitting detection, model evaluation, and feature selection.

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Taught by

Farid Taba

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Subjects

Computer Science