A Unified Approach to Interpreting Model Predictions is a research paper that presents a comprehensive framework for interpreting the predictions made by machine learning models. The main goal of this approach is to provide a unified and systematic way to understand why a model makes specific predictions. The paper discusses various methods and techniques that can be applied across different types of models, such as linear models, decision trees, neural networks, etc., to gain insights into their decision-making processes. This approach is important because it helps address the "black-box" nature of complex models by making their predictions more transparent and interpretable.

There are many factors that can affect a parent’s decision to move to a specific school district or send their child to a certain school. There are also numerous resources to consult if looking for advice or resources that rank schools to aid in this decision-making process. One popular resource is GreatSchools.org. They rank schools on a scale from 1 (lowest) to 10 (highest) based on test scores, student progress, and equity. U.S. News & World Report also publishes lists of the best schools in specific cities and states. They rank schools based on college readiness, state assessment proficiency, state assessment performance, underserved student performance, college curriculum breadth, and college readiness. For most parents, all these factors are likely very important to consider when deciding what school their child should attend. However, neither school ranking system considers student experience outside the realm of the curriculum and test taking. By looking at just one of the many sites that rank schools, GreatSchools.org, our goal is to bridge the gap and determine if there is a relationship between student experiences and school ratings.

Machine learning plays a vital role in trading by enabling the analysis of vast amounts of financial data and the development of predictive models. It leverages algorithms and statistical techniques to identify patterns, make predictions, and generate insights for informed trading decisions. Machine learning algorithms can be applied to various aspects of trading, including price prediction, risk management, portfolio optimization, market analysis, and automated trading. By leveraging machine learning, traders can uncover hidden patterns in data, adapt to changing market conditions, and improve decision-making processes, ultimately aiming to achieve better trading performance and profitability.

This is my notes on Georgia Tech's ISYE 6669: Deterministic Optimization. Optimization is the process of adjusting a system to achieve the best possible performance or outcome. Deterministic (non-stochastic) optimization is a mathematical approach to finding the best solution to a problem by systematically searching the solution space for the optimal outcome. The optimization process is based on a set of deterministic (i.e., non-random) rules and algorithms, and the result of the optimization process is unique and repeatable.