Machine Learning

Workload: 9/10

Difficulty: 6/10

Enjoyment: 8/10

Relevance: 10/10

Machine learning is the lynchpin course of the machine learning specialization and provides a broad survey of the subject. The pacing of the course was breakneck as it attempted to teach both implementation and theory simultaneously through video lectures, assigned readings and exhaustive assignments. The assignments were valuable tools for learning but the grading, as many other reviews mention, is hard and opaque. Time and effort spent on an assignment were not strongly correlated with the grade I would receive which is frustrating and I spent far too much time writing high quality code for the earlier assignments. I believe the course is designed with Gen AI in mind as during the two week gap between assignment students are expected to implement five to ten machine learning models on multiple datasets and then write a ten page report detailing their experiments and drawing conclusions. In one particularly frustrating experience I submitted an assignment that I had spent 30 hours and only to learn after the deadline that the conversion from work to PDF caused the text to block my graphs. There is no option for late submission with penalty and no requests for regrade due to submission issues. I understand this is an online masters level course but this policy still seems harsh and is not shared by other courses in the program. Overall, this was a great and sometimes painful learning experience.

AI Ethics & Society

Workload: 3/10

Difficulty: 3/10

Enjoyment: 6/10

Relevance: 3/10

AI Ethics and society covers the moral and social implications of the increased integration of AI into our lives. It encourages algorithmic transparency and human centric design through lectures, case studies, data analysis exercises and multiple choice tests. I thought that the course material was interested but not well suited to an online format. Canvas discussion board posts will never be as engaging as a live discussion and much of the material in this course falls in a grey area that lends itself to nuanced conversations. The result is a course in which you will get our of it what you put it, but one that does not require you to put very much in.