Predictive Modeling in AI

Understanding Predictive AI

Explore why predictive modeling is a critical tool, touching on its capabilities to inform decisions and forecast outcomes across various fields.

Point Prediction and Classification Techniques

Apply leading point prediction and classification techniques such as regression analysis, random forests, support vector machines, and neural networks.

Time Series Analysis

Unpack the complexity of time series data and learn how to make predictions over time by focusing on understanding the patterns, trends, and seasonality in data and how that changes over time.

Data Embeddings and Multitask Learning

Learn how to improve the performance of predictive models through techniques such as data embeddings, multitask learning, and feature engineering

Handling Missing Data through Imputation

Tackle the challenge of incomplete datasets with imputation techniques to fill in missing values, ensuring your predictive models are both robust and reliable.

Confidence Estimation in Predictions

Gain confidence in your predictive models by learning how to estimate the uncertainty and reliability of their predictions. This skill will help assess the quality of your model's output and make informed decisions based on predictive analysis.

Applications Across Domains

Apply your predictive modeling skills to real-world scenarios in various domains, such as computer vision, natural language processing, and tabular data analysis, showcasing the applicability of predictive modeling techniques in solving practical problems.

Building Robust and Reliable AI

Explore the principles of robustness in AI systems, focusing on creating models that are resilient against missing and incorrect data, misconceived or malicious settings, and adversarial attacks. Learn about techniques to mitigate these problems, estimate the confidence in the AI’s solutions, and ensure that AI performs consistently under various conditions.