This activity is a field investigation where students explore different types of trees and identify them using leaves and classification tools.

This activity is a group activity where students investigate leaves and sort them based on established criteria.

This activity is a guided inquiry where students will find their own lichen and classify it into one of three categories. They will collect, analyze, and present their finding to the class.

This is a communication intensive supplement to Linear Algebra (18.06). The main emphasis is on the methods of creating rigorous and elegant proofs and presenting them clearly in writing.

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks.

In this math lesson, learners discover a powerful way to display dataäóîusing a glyph. Learners create glyphs by drawing their own faces on paper plates. Making a glyph involves the logical skill of classification--a learner must decide which groups he or she belongs to in order to complete the picture. This lesson guide includes questions for learners, assessment options, extensions, and reflection questions.

This activity is a classroom guided inquiry lesson where the student will sort, identify, and classify Minnesota fish.

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

This activity is a field investigation where students identify native MN plants and record the common name, scientific name, and important information about each.

In this math activity, learners classify polygons according to more than one property at a time. In the context of a game, learners move from a simple description of shapes to an analysis of how properties are related. This lesson guide includes sample steps in the game and extensions.

This is an activity on polymers and plastics to expand our study of physical/chemical properties and changes.

This activity is a field-based investigation involving identifying and investigating common trees on the schoolyard and creating a field guide containing pressed leaves.

This activity allows students to explore the variety of objects found in the Solar System, and to create their own logical categories for them based on observation of the object's characteristics.

This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

At this stage of the "Lost in the Amazon" (hypothetical) adventure, students determine what supplies they will take with them to survive their trip through the Amazon. They use estimation and basic math skills to determine how much they can carry and what they can use to survive in the jungle environment as they travel on to their destination.

The main aims of this seminar will be to go over the classification of surfaces (Enriques-Castelnuovo for characteristic zero, Bombieri-Mumford for characteristic p), while working out plenty of examples, and treating their geometry and arithmetic as far as possible.

This activity is a teacher-guided inquiry activity of the sorting or grouping of Minnesota critters according to student driven criteria or purpose of their groupings. Teacher/student questions and discussion should be encouraged after this activity to emphasize that awareness of the criteria or purpose of certain groupings may be important before beginning an investigation.

This activity allows students to investigate classification and create their own dichotomous key. Discussion will include classification of items in everyday life.

This lesson helps grade 3 students understand the difference between Vertebrates and Invertebrates.