Instructor: Pavlos Protopapas, Scientific Program Director and Lecturer at the Harvard John A. Paulson School of Engineering and Applied Sciences.
Course Introduction
Welcome! The objective of this course is to serve as a ‘refresher’ for fundamental concepts in math, statistics, and programming required to undertake a course in machine learning, data science, or AI similar to Harvard’s CS109a (Data Science 1) or an equivalent course.
The Bedrock course consists primarily of pre-recorded lectures and tutorial videos with accompanying coding exercises and quizzes for self-assessment.
Participation is entirely optional and you can progress through the material at your own pace. This course will have no effect on any grades nor on your standing within your program.
As you progress in your learning journey, our friendly teaching staff will be here to support you through the course discussion forum as well as live online office hours.
You can use the sign-up form linked above to request access to the course. Once approved, you’ll receive an email with further instructions.
Course Overview
- Basic Python: Data types, data structures, functions
- Advanced Python: Classes, dunder methods, decorators
- Python Libraries: Pandas, Matplotlib, Numpy, SKLearn
- Basic Probability & Statistics: Random Variable, Probability Density Functions and Mass Density Functions, point estimates
- Basic Linear Algebra & Calculus
Some prior programming experience in any language and previous exposure to calculus is assumed. Again, the goal is to refresh those topics that you feel have become a bit ‘rusty’ and/or to fill in any gaps in your understanding. Those starting from scratch with no previous experience will likely find things move too fast for them.
Course Components
Lessons
The learning materials for each topic are bundled. Lessons will help students develop the intuition for core concepts, providing the necessary mathematical background, guidance on technical details, and relevant examples.
Reading Assignments and Video Assignments
Lessons include readings, lecture slides, and lecture videos. The goal of the reading is to prepare you for the lesson content, familiarize you with new terminology and definitions, and to help you determine which part of the subject may need more attention.
Each session may have a short ‘reading check’ at the beginning which covers the assigned reading for that week. This will help assess your understanding of the material.
Quizzes & Exercises
Most lessons also include exercises and quizzes. Quizzes are for self-assessment. The exercises can be attempted as many times as you wish.
Course Policies
Getting Help
For questions about the course content, after you have tried to troubleshoot yourselves, the process to get help is:
- Post the question on the course discussion forum. Note that forum posts are visible to everyone. The teaching staff monitors the posts. You are encouraged to answer your peers’ questions! 😁
- Attend an online office hour (schedule TBD).
Diversity and Inclusion Statement
We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions [1].
We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.
We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:
- Offensive comments related to age, race, religion, creed, color, gender (including transgender/gender identity/gender expression), sexual orientation, medical condition, physical or intellectual disability, pregnancy, or medical conditions, national origin or ancestry.
- Intimidation, personal attacks, harassment, unnecessary disruption of talks during any of the learning activities.
Reference:
[1] K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.