Class Projects
Students will build, test, and publish their own game in KTBlocks
CODING PLATFORM
The KTCoder all-in-one coding platform supports our interactive online classes, our specialized curriculum, and our students’ passion for learning.
STUDENT HELP HOURS
Help hours are led by our highly qualified teaching assistants. It is an easy and free way to get immediate feedback on your code.
PROGRESS REPORTS
KTBYTE will e-mail parents with behavior and grade progess reports.
COMPLETION CERTIFICATES
Students can request a certificate of completion once they finish each course.
Class Description:
This course provides group tutoring for data-science science-fair projects.
Detailed Description: [AI 3] is an independent practicum, where each student spends the entire course doing one large research project. While [AI 2] data sets are from past projects in the AI literature, [AI 3] data sets are usually completely novel. Students are expected to crawl, collect, or simulate systems to generate the data they need. Unlike previous courses, there are no problem sets at all. Instead, the instructor will provide direction to keep the student on track towards producing a final deliverable, such as a research paper or presentation. This is also the only KTBYTE course that features live tutoring with the instructor as a regularly scheduled part of the curriculum. Students may repeat [AI 3] for multiple semesters or years as needed to complete or expand their projects. Besides machine learning, students will understand the process of scientific inquiry and all the myriad of time management and communications skills that are required to learn on their own.
You should only take this class if you are proficient at:
- writing independently from scratch python programs of more than 300 lines that include if statements, for loops, class definitions, importing and working with third party libraries, ability to read a python library API.
- building Deep Learning models using Keras,
- working at some capacity with source code written by others,
- downloading, pre-process, importing a dataset by herself,
- is able to define a model with inputs/outputs, (high level understanding of what is a loss function, the difference between regression)
- Has some understanding of different classes of problems (e.g. supervised/unsupervised)
- is comfortable with a terminal
Research Projects from KTBYTE students
Prerequisites
Completion of [AI 2] and permission of instructor
Class Description:
This course provides group tutoring for data-science science-fair projects.
Detailed Description: [AI 3] is an independent practicum, where each student spends the entire course doing one large research project. While [AI 2] data sets are from past projects in the AI literature, [AI 3] data sets are usually completely novel. Students are expected to crawl, collect, or simulate systems to generate the data they need. Unlike previous courses, there are no problem sets at all. Instead, the instructor will provide direction to keep the student on track towards producing a final deliverable, such as a research paper or presentation. This is also the only KTBYTE course that features live tutoring with the instructor as a regularly scheduled part of the curriculum. Students may repeat [AI 3] for multiple semesters or years as needed to complete or expand their projects. Besides machine learning, students will understand the process of scientific inquiry and all the myriad of time management and communications skills that are required to learn on their own.
You should only take this class if you are proficient at:
- writing independently from scratch python programs of more than 300 lines that include if statements, for loops, class definitions, importing and working with third party libraries, ability to read a python library API.
- building Deep Learning models using Keras,
- working at some capacity with source code written by others,
- downloading, pre-process, importing a dataset by herself,
- is able to define a model with inputs/outputs, (high level understanding of what is a loss function, the difference between regression)
- Has some understanding of different classes of problems (e.g. supervised/unsupervised)
- is comfortable with a terminal
Prerequisites:
Completion of [AI 2] and permission of instructor