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Module 3 Introduction

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Illinois Tech

Statistical Learning

Course 2 of 3 in the Introduction to Data Science Techniques Specialization

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This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills. Our journey is guided by the well-known textbook "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani, and J. Friedman. This course provides examples written in Python. Your system should have Python 3.8 or higher, as well as essential libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and PyTorch. These tools not only support the learning process but also prepare you for real-world data analysis challenges. Whether you're aiming to refine your expertise or just starting out in the field of data science, this course provides the knowledge and tools to transform your understanding and application of statistical learning. It's a perfect blend of theory and practice, ideal for anyone looking to enhance their skills in data interpretation and analysis.

Skills You'll Learn

Data Analysis, Supervised Learning, Regression Analysis, Classification And Regression Tree (CART), Machine Learning, Unsupervised Learning, Data Science, Predictive Modeling, Feature Engineering, Statistical Inference, Statistical Machine Learning, Statistical Programming, Statistical Analysis, Statistical Methods

From the lesson

Module 3: Linear Classification Methods

Welcome to Module 3 of Math 569: Statistical Learning, where we delve into linear classification. In Lesson 1, we explore how linear regression, typically used for predicting continuous outcomes, can be adapted for classification tasks-predicting discrete categories. We'll cover the conversion of categorical data into a numerical format suitable for classification and introduce essential classification metrics such as accuracy, precision, and recall. In Lesson 2, we'll explore Linear Discriminant Analysis (LDA) as an alternative method for constructing linear classifications. This method introduces the notion that classification maximizes the probability of a category given a data point, a framing we will revisit later in the course. Maximizing the likelihood of classification, given some simplifying assumptions, leads to a linear model that can also reduce the dimensionality of the problem. Finally, in Lesson 3, we will cover logistic regression, which is constructed by assuming the log-likelihood odds are linear models. The outcome, similar to LDA, produces a linear decision boundary.

Module 3 Introduction1:47

Taught By

Shahrzad (Sarah) Jamshidi

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