Do you enjoy thinking about what it means to "think"? Or thinking about why you decided to think instead of deciding not to think? Then you might think it might be a good idea to read on... This is meant as a philosophy series -- that is, it does not try to be useful in every-day life; rather, we'd be looking at a number of ideas about decision-making, knowledge, science, and so on, simply for the joy of thinking about these things. And because I really hate monologuing, my plan is to pick a bunch of topics and have each learner do a short 10-minute presentation on a topic (or more if you wish so, by all means!), so that we can all learn from one another. Some topics I'd like to cover: What does it mean to know something, and how do we acquire knowledge? What is information theory and what does it say about acquiring knowledge? And once you acquire knowledge, how can you make good decisions based off of it? What does it mean for a decision to be good anyway? And how could we possibly connect all of this to the Baron Münchhausen, Sleeping Beauty, and the Ugly Duckling? (hint -- Google each of these three together with the keywords "wiki" and "philosophy")

We are currently working through the first 100 Project Euler problems. Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. https://projecteuler.net/ We have also reviewed using Python to solve Khan Academy problems; this was a teaching session which I may repeat by popular demand. We will be using Python (version 3.8 or higher) and the numpy and sympy packages. I strongly recommend installing and using Anaconda Spyder for this series: https://www.anaconda.com/products/individual Alternatively, you can use an online Python session like: https://www.pythonanywhere.com/try-ipython/ Please make sure you can run successfully (i.e. with no error) before registering for this series: import numpy as np, sympy Based on participant interest, we can look at some advanced topics: Computing limits, derivatives, and integrals numerically; Linear algebra; How computers represent numbers internally and numerical stability; Optimization