Note: If you come for the Math workshop, you can stay for the Python workshop. No need to pay twice.
During this workshop, we will collaboratively learn and build programmatic frameworks in python from basic concepts such as probability theory and game theory. We function as a support group to help people learn to program and about machine learning faster.
Until we find a location with a conference room and wifi, we will have to run the workshop out of Think Coffee. The coffeeshop is a public place with variable seating. Hence, seating is not guaranteed. Only pay if you can find a place to sit. If you know of a place that would like to host us, please message me. The reason for the price is two fold. One, it helps with demand. Two, it raises cash and tests for viability to rent a hall or at WeWork.
We will be starting from scratch to learn programming. We will create a Texas Hold'em Poker app over the next couple of weeks while learning. We will be adding more complex features including an AI that plays against the players and learns over time. As we go along, we will be also learning the fundamentals of probability theory, statistics, AI, data science, and computer science.
Materials and Resources:
Here is the GitHub link to the tutorial repository: https://goo.gl/5kjD5L
Feel free to comment and add suggestions: https://goo.gl/JPQ2se
More resources that we will be using can be found here: https://goo.gl/bFgnJQ
The first couple of weeks we will be focusing on the basics of python, and learn how to create a working program inside Jupyter: the python development environment we will be using.
Key Goals of this Course:
1) How to be comfortable with python and the command-line interface,
2) The basics of software engineering.
Style of Teaching:
We have adopted an agile dev-ops mindset to learning. Keep in mind, we are building features and lessons as we progress. So be patient. It is going to be different from a structured class-room lecture format.
Why Gambling or Card Games:
The historical foundations of probability theory are based on gamblers trying to determine likelihood of winning. By using card games as a foundation, it reduces the need for domain specific knowledge. There are no real monetary transactions on gambling during this course. Therefore this will be a fun and intuitive way to learn mathematics behind probability theory and game theory, without the often unspoken dark-side of gambling -- addiction.
We employ a concepts of groups. There are different groups for different skill levels. Newbies after the lecture will be placed into their own groups where there will have to complete the following requirements to move onto another group:
-Finish Codecademy's Python track
-Read Crash Course Python
-Read Think Python
-Do Hacker Rank's Python Track
-Review our Github notebooks: https://github.com/tesla809/intro-to-python-jupyter-notebooks
The more you do before hand the better. However, you can do this in the workshop as well. We are more of a support group to help people learn to code than an actual class.
If you have a great grasp of the language then you can either jump on hacker rank and test your skills out, or begin to work on the Poker app (or a project of your choice). I will be there to help you along your journey.
All group will have their own sub gitter as well.
More advanced groups:
We will be using edX and MIT OCW to learn
Data Structures and Algorithms:
Word of Caution:
The goal of this group is to learn computer programming, and not the promotion of gambling. We treat problem gambling as a serious social affliction and encourage individuals with this problem to actively seek help. If you think you have a problem with gambling or you have a history of gambling problems, please seek help. There are organizations like the National Council on Problem Gambling (http://www.ncpgambling.org) that provide resources to deal with gambling problems. Some of these resources can also be accessed using The National Problem Gambling Helpline Network at[masked].