This course was designed to have virtually no prerequisites in regards to participation and completion of the course. So at a base level, anyone can find something to take away from this from this course. It is similar to a survey course but we will zoom in at some technical aspects on both the theory and the practice of machine learning.
Outcomes and Objectives
Differentiate between Artificial Intelligence (AI) and Machine Learning (ML)
Locate a broad range of Machine Learning applications and their impact on individuals and systems
Explain how Machine Learning algorithms make predictions
Identify the skills, tools and concepts helpful for continued learning in the field of Machine Learning
Describe several basic Machine Learning algorithms and conceptualize systems for their application
Computer – At a minimal level of engagement all that is necessary is a computer or mobile phone and the ability to view the short lectures and reflect on the content.
Journal – Most modules have some prompts for reflection and we recommend also using either a physical or digital journal to write responses to the prompts.
Software – If you have some experience in programming (or would like some) labs are provided in pdf form for step by step work through. Look for them in the Research Springboard sections. The software requirements vary but a list is provided in the software tab in this module.
Time – Each module contains some curated links to expand on the topics covered in the lecture. The lists are by no means comprehensive and we hope they will act as a spring board into your own research as it suits your interests.
What this course is not
Due to the broad scope and lack of prerequisites this course is mostly non-technical and does not cover implementing any machine learning from scratch. However, after completion of this course and with some intermediate skills in the domain of those covered in the “Skills” sections (1 and 2) one should be ready to make the most out of a more technical machine learning course.