Dimensionality reduction homework
General Information Instructor: Dr. Yoonsuck Choe Email: choe a tamu. Lectures: TR amam, ONLINE, Synchronous you will receive the zoom link via email Introduction: Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, Bayesian learning, artificial neural networks, instance-based learning, genetic algorithms, reinforcement learning, unsupervised learning, and biologically motivated learning algorithms. Goal: The goal of this course is to help you to learn the theoretical foundations of machine learning, learn various problems and solution strategies in machine learning, and learn practical methodology for applying ML algorithms to problem domain of your choice. Objectives: The expected accomplishments of the students are as follows: Become confident in the basic mathematics and algorithms underlying machine learning.
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In machine learning, the performance of a model only benefits from more features up until a certain point. The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely. There are multiple techniques that can be used to fight overfitting , but dimensionality reduction is one of the most effective techniques. Dimensionality reduction selects the most important components of the feature space, preserving them and dropping the other components. There are a few reasons that dimensionality reduction is used in machine learning: to combat computational cost, to control overfitting, and to visualize and help interpret high dimensional data sets. Often in machine learning, the more features that are present in the dataset the better a classifier can learn.
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CS590: Randomized Algorithms, Fall 2020
Time : MW 2. This course is designed for MS students in computer science. Lectures will focus on developing a mathematical and algorithmic understanding of the methods commonly employed to solve unsupervised machine learning and data mining problems. Students will also collaborate on a project in which they must complete a data analysis taks from start to finish, including pre-processing of data, analysis, and visualization of results.
In situations where the complexity of the data is too much either for humans to understand, or computers to process in a time efficient manner, we often want to reduce the number of features dimensions in the dataset. When doing Dimensionality Reduction, we want to keep as much useful information as possible. Interesting and useful datasets are often though not always rather large. They generally have both many observations for example, data on 90 million homes recently listed on Zillow.
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