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Practicum in Artificial Intelligence
Before any discounts or coupons
for 18 hours
One student-based project.
Virtual Machine (VM)
A Virtual Machine is a remote desktop that allows students to connect to it from anywhere. We provide VMs so that students use it during classes and to work on homework.
1-1 Tutoring with Teacher
Student Progress Report
Students will get personalized progress reports and feedback from the instructor
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)