Foundations of Machine Learning

Pre Requisites

None required. If you are taking this course as part of the Artificial Intelligence Foundations Program of Study, Artificial Intelligence in the World, Applications of Artificial Intelligence, and Procedural Programming should be taken first.

Description

In this course, you will deepen your understanding of machine learning. You will examine how and why the concept of machine learning was developed. Excel and Python will be used to analyze data and training models. Finally, you will discover what the future of machine learning looks like and the importance of the development cycle.

Foundations of Machine Learning is the fourth course in the Artificial Intelligence (AI) Foundations program of study in the Engineering Technology cluster.

Follow the link below for the Department of Education Career & Technical Education Curriculum Frameworks:

https://www.fldoe.org/academics/career-adult-edu/career-tech-edu/curriculum-frameworks/

Segment One:

• Machine learning vs. human learning

• Abstraction

• Types of representations

• Data structures

• Search algorithms

• AI vendors

• Supervised, unsupervised, and reinforcement learning

• Learning algorithms

• Classification

• Neural networks

• Training models with data

• Personal, geospatial, time-based data, and company

Segment Two:

• Problem solving and data

• APIs, RSSs, and web scraping

• SQL and NoSQL databases

• Data wrangling

• Statistical sampling and testing

• Identifying patterns in data

• Data analysis techniques

• ML model building

• Errors in decisions and predictions

• Privacy and security concerns with data

• ML development process

• Adjust and evaluate the model

• Ethical problems related to ML

• GPUs and CPUs

• Fairness in AI

• Privacy and security concerns

None required.

Besides engaging students in challenging curriculum, the course guides students to reflect on their learning and evaluate their progress through a variety of assessments. Assessments can be in the form of practice lessons, multiple choice questions, writing assignments, projects, research papers, oral assessments, and discussions. This course will use the state-approved grading scale. Each course contains a mandatory final exam or culminating project that will be weighted at 20% of the student’s overall grade.***

***Proctored exams can be requested by FLVS at any time and for any reason in an effort to ensure academic integrity. When a proctored exam is administered to assess a student’s integrity, the student must pass the exam with at least a 59.5% to earn credit for the course.

To achieve success, students are expected to submit work in each course weekly. Students can learn at their own pace; however, “any pace” still means that students must make progress in the course every week. To measure learning, students complete self-checks, practice lessons, multiple choice questions, projects, discussion-based assessments, and discussions. Students are expected to maintain regular contact with teachers; the minimum requirement is monthly. When teachers, students, and parents work together, students are successful.

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Courses subject to availability.

Pursuant to s. 1002.20, F.S.; A public school student whose parent makes written request to the school principal shall be exempted from the teaching of reproductive health or any disease, including HIV/AIDS, in accordance with the provisions of s. 1003.42(3). Learn more about the process and which courses contain subject matter where an exemption request can be made.

High Foundations of Machine Learning

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High Foundations of Machine Learning

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