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.

Literacy is an important aspect of science. To be literate in science means students are able to understand, read, and write in terms of science. This lesson is designed to get students to think critically about real world application. The lesson incorporates technology and Bloom’s highest level of thinking, creativity. Students will learn about writing scientific names of organisms and classifying organisms, how organisms interact with each other and their environment, and the impact of natural disasters.

Freshmen enrolled in the Spaceship Earth Living Learning Community conduct research on a real project that is formulated and conducted during a 2-semester academic year.

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Students use specimens prepared by the teacher and try to decide if each one is living, non-living or dead.  This may be done as inquiry prior to instruction or as reinforcement.

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

The purpose of the resource is to produce a land cover type map from hard copies of Landsat satellite images.

In this lesson the students will go on a scavenger hunt trying to find objects that have definite physical properties such as shape, mass temperature, texture and flexibility.

In this lesson the students will observe and record properties of rocks based on properties such as size, weight, shape, color or texture.

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

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"For about 40 minutes in 2013, the world was in panic. Cloud services for internet retail giant Amazon had crashed. In less than an hour, Amazon had lost an estimated 5 million dollars in sales. It’s an operational standstill feared by cloud users and operators alike. Unfortunately, most methods for detecting the faults that lead to such crashes tend to be inefficient and inaccurate. But now, a team of researchers from China, Saudi Arabia, and the US has developed one of the best detection methods yet—based on a machine learning tool known as a support vector machine. A support vector machine is a classification-based learning algorithm. For the simple task of classifying circles by color, the algorithm takes a set of examples and determines the dividing line or plane that maximizes the separation between the two classes. That provides the widest margin of classification error, preventing any new circles from being mislabeled..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Stratigraphy students are tasked with documenting an outcrop (sketches, photographs, and rock descriptions) and providing a three panel display of the results. Students work on the lab in class and receive direct mentoring in rock and outcrop description, then for homework they craft digital professional quality reports to present the data to others. These reports are graded, then returned to everyone simultaneously so they can be placed side by side. Students directly observe how others interpreted the same deposits, which underscores the concepts of sound data documentation, and separating data from interpretations.

Lab: Particle Size Analysis, Soil Texture, and Hydraulic Conductivity

(Note: this resource was added to OER Commons as part of a batch upload of over 2,200 records. If you notice an issue with the quality of the metadata, please let us know by using the 'report' button and we will flag it for consideration.)

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the 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. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

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.

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

Students will determine a rule for classifying outdoor objects based on physical properties of matter.

Students will determine a rule for classifying outdoor objects based on physical properties of matter.