Overview
IDS:705 Principles of Machine Learning
Course Summary
In almost every field, there is a need to draw inference from or make decisions based on data. The goal of this course is to provide an introduction to machine learning that is approachable to diverse disciplines and empowers students to become proficient in the foundational concepts and tools. You will learn to (a) structure a machine learning problems and determine which algorithmic tools are appropriate, (b) evaluate the performance of your solution using field-appropriate metrics and practices, and (c) accurately interpret your model output and communicate your results to interdisciplinary audiences. This course is a fast-paced, applied introduction to machine learning that through extensive practice with foundational tools, helps you to develop your knowledge of foundational machine learning concepts, and provides practical experience with those tools to prepare you for practice or future study.
Detailed description
Machine learning is a collection of useful tools for understanding and making decisions based on data and past experience; it is not a hammer to be applied to every nail, but rather a precision tool to be used when needed. This course will begin with exploring the purpose of machine learning told through a discussion of the types of problems that machine learning can answer: describing, predicting, and strategizing based on data and the tools at our disposal to address these challenges: supervised learning including classification and regression; unsupervised learning including clustering and density estimation; and reinforcement learning. There will be a strong focus on how to formulate a machine learning problem. Central to that formulation will be developing an understanding of how to preprocess data for analysis (e.g. feature extraction/dimensionality reduction, training/validation data sampling), model selection, and performance evaluation with cross validation. The final topic of this course will be a brief overview of state-of-the-art machine learning techniques that are emerging in the field.
Throughout this course, the focus will be on applying algorithms rather than diving deeply into theory. You will be asked to consider the practical issues of machine learning problem solving: challenges of applying machine learning code packages, striving for parsimony (simplicity of models) and interpretability, and ensuring model assumptions are valid for a given problem and dataset. This course will also stress the importance of team-based collaboration, the value of producing fully reproducible and validated results, and tools to help with both such as version control and code repositories.
Communicating your results. Data science solutions are only as impactful as the communicator who shares them: therefore communication of your findings will be a core component of this course. Demonstrating competency in data science means (a) exhibiting a working knowledge of technical concepts including programming, statistics, and mathematics and (b) being able to clearly communicate the problem you were trying to solve or question you were trying to answer, why it matters, and how well your analysis worked. You will have opportunities to practice these skills throughout this course in the context of interpreting and sharing the results of your analyses.