Computer Science-related projects
| Grade Level | Applicable Standards | Module Summary | Year | Developer | Link |
|---|---|---|---|---|---|
| 9 | Indiana: 6-8.DI.1, 6-8.DI.3, 6-8.DI.4, 6-8.CD.3, 6-8.CD.4, 6-8.IC.1, 6-8.IC.2, IED-1.1, IED-5.3, IED-5.4, IED-5.5, IED-5.6; ITEEA: 3, 4, 17, HS-ETS-1-1, HS-ETS-1-3 | Many of us use fingerprint identification for security on our smartphones. Facial recognition is similar. Just like your fingerprint is unique to an individual, so are the landmarking points on a face. Facial and image recognition is an emerging technology in the research fields of computer science. Facial recognition is having computer identify a person for hospitality and security. The hospitality occupations are using facial recognition to help the customer feel comfortable in the new or returning environment. For example when a customer returns to a restaurant, a computer can remind the staff what the patron ate on the previous visits. Security is also a large field in facial recognition. Facial recognition can be used to detect criminals and avoid crimes before they occur. Now a team of University of Notre Dame biometrics experts is developing a crime-fighting tool that can help law enforcement officials identify suspicious individuals at crime scenes in a group of people. Facial recognition is also being created to monitor the trustworthiness and dominance of individuals similar to body language. Facial recognition is also being used to identify people for security purposes | 2016 |
Seth Ponder, Riley High School |
Zip Archive (link) |
| 10, 11, 12 | ITEEA 9-12: 2.AA, 4I, 9K; CCSS 9-12: A.SSE.1, F.1F.4 | Students in AP Computer Science A will study and analyze the strengths (simplicity, memory usage), weaknesses (speed, memory usage), and implementation in Java of sorting algorithms, including: Bubble Sort (as an introduction), Selection Sort, Insertion Sort, and Merge Sort. | 2018 |
Tom Falcone La Lumiere School |
Presentation (pdf) |
| 11, 12 | Indiana: CSII-1.5, CSII-1.6, CSII-2.1, CSII-2.1, CSII-2.3, CSII-2.4, CSII-3.2, CSII-3.5, CSII-3.7, CSII-3.10, CSII-5.1, CSII-5.2, CSII-5.4, CSII-7.3, ITEEA: 1, 3, 4, 8, 10, 11 | This Machine Learning module will enrich the Computer Science curriculum by adding insight into current computer science methods related to cutting edge technologies and products. The module will present the big picture of the AI field, its impact on business and society, and its future potential. It will provide students the opportunity to experience and experiment with samples of machine learning code and devices that display examples of number, image, facial and voice recognition. Students will be introduced to the principles of convolutional neural network software and will modify and run provided code to alter the machine learning process. The module will be integrated through multiple units of the Computer Science II class. | 2018 |
Nancy Duncan Penn High School |
Zip Archive (link) |
| 11, 12 | ITEEA: 2.CC, 3.J, 4.H; Indiana Computer Science: 6-8 DI-5, 6-8 CD-4, C-8 IC.2; CSTA: 3A-3, 3B-5, 3B-9, 3B-6, 3B-5, 3A-2, 3B-2 | Machine Learning, where a program “learns” to recognize a set of conditions by repeatedly sampling, attempting to classify, and correcting the attempt, while progressively minimizing the error, is a rapidly growing field of research and development. Students will learn how Machine Learning is being used in science and business, then they will learn hands-on how to read and classify data. | 2017 |
Tom Falcone La Lumiere School |
Zip Archive (link) |
| 9, 10 | Indiana: CSII-1.5, CSII-2.4, CSII-6.1, CSII-6.2; ITEEA: The Nature of Technology, Technology and Society, Design, Abilities for a Technological World; CSTA: 3A-AP13; 3A-AP-18, 3A-AP-23; 3A-IC-24 | In this module, computer science students will investigate current applications of machine learning and artificial intelligence as it applies to the Google AIY kits, apps like photomath, and Siri. Students will take a closer look at how image classification occurs in python using TensorFlow by executing the provided MNIST and CIFAR scripts. In order to better understand how the scripts function, students will explore the TensorFlow and ML tutorial resources available online. Students will demonstrate their understanding of the programming constructs and ML principles by training their own miniature version of MNIST. Their final deliverable will be a presentation of their code and a reflection of their learning process throughout the module. | 2018 |
Lindsay Moore Plymouth High School |
Zip Archive (link) |
| 11, 12 | CSTA: 3A-DA-12; 3B-DA-05; 3B-DA-06; Indiana: CSI-3.2; CSI-6; CSI-7.2; CSI-8.4 | Data is all around us, and it can be powerfully harnessed to answer interesting questions about the world that we live in. But how can we get computers to help us with this? How can computers make inferences from thousands of bits of information, in order to answer interesting questions? In this module, students will be introduced to artificial neural networks, and train one to answer an interesting image classification question. Students will be able to (1) train an ANN using the MNIST data set and use it to classify handwritten numbers provided by students, and (2) gather their own dataset and train another ANN to classify images from their dataset. | 2019 |
Kristen Haubold Riley High School |
Module Summay (pdf) |
| 11, 12 | CSTA: 3A-DA-12; 3B-DA-05; 3B-DA-06 | Convolutional neural networks in Python |
Beth Marchant Adams High School |
Zip Archive (link) |