INTRODUCTION TO MACHINE LEARNING AND STATISTICAL PATTERN CLASSIFICATION
Pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, covers fundamental algorithms and selections of contemporary, current state-of-the-art algorithms. Focus on evaluation of machine learning models using statistical methods. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, algorithmic and nonparametric approaches. Practical use of machine learning algorithms using open source libraries from the Python programming ecosystem.
Prerequisites & course information
Requisites: MATH 320 , 321 , 340 , 341 , 345 , 375 , graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Understand the different subfields of machine learning, such as supervised and unsupervised learning and being familiar with essential algorithms from each subfield. Audience: Undergraduate 2. Identify whether machine learning is appropriate for solving a given problem task and which class of algorithms is best suited for real-world problem solving. Audience: Undergraduate 3. Use statistical learning theory to combine multiple machine learning models via ensemble methods. Audience: Undergraduate 4. Apply best-practices for statistical model evaluation, model selection and algorithm comparisons including suitable statistical hypothesis tests. Audience: Undergraduate 5. Use contemporary programming languages and machine learning libraries for implementing machine learning algorithms such that they can be readily applied for practical problem solving. Audience: Undergraduate 6. Connect concepts from probability theory with supervised learning by implementing models based on Bayes' theorem. Audience: Undergraduate Learning Outcomes: 1. Understand the different subfields of machine learning, such as supervised and unsupervised learning and being familiar with essential algorithms from each subfield. Audience: Undergraduate 2. Identify whether machine learning is appropriate for solving a given problem task and which class of algorithms is best suited for real-world problem solving. Audience: Undergraduate 3. Use statistical learning theory to combine multiple machine learning models via ensemble methods. Audience: Undergraduate 4. Apply best-practices for statistical model evaluation, model selection and algorithm comparisons including suitable statistical hypothesis tests. Audience: Undergraduate 5. Use contemporary programming languages and machine learning libraries for implementing machine learning algorithms such that they can be readily applied for practical problem solving. Audience: Undergraduate 6. Connect concepts from probability theory with supervised learning by implementing models based on Bayes' theorem. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027