Capstone

MIT Missing Semester

Command Line Basics

SK: Chapter 3

3.1.2, 3.2.2, 3.3.2, 3.4.2

MIT Missing Semester

Working With Unix

Take the SURVEY

Technology over time (courtesy of [FILL THIS IN])

Directions to install Windows Subsystem for Linux (WSL) are here: https://docs.microsoft.com/en-us/windows/wsl/install-win10

4.1.2, 4.2.2, 4.3.7, 4.6.2

Not a bad idea to take a look the sections without exercises if you have time.

MIT Missing Semester

Bash Programming

This is a longer suite of exercises; be efficient with your time!

5.1.2, write a script that takes three inputs and adds them, 5.2.2, 5.3.2, 5.4.5, 5.5.2, 5.6.2, 5.7.5, 5.8.4, 5.9.5

Fighting the distraction problem: apps to review

9/3--9/6

Download the PyCharm IDE (If you have programmed in Python and prefer another editor, that's fine)

Consider setting up GitHub as it is super useful for version control and is commonplace in industry. Also consider getting GitHub Copilot, which uses generative AI to help you produce code more quickly.

This Coursera class has the nerdiest profs I've ever seen, but the content is excellent. If you do both parts 1 and 2, you end up writing Asteroids and learning a lot

It never hurts to Google something like "learning Python for free"; I honestly don't know which books/courses to suggest beyond the Coursera one, above; suggestions welcome

Find a solution to the parking problem using Python

Write Randp in Python.

Solve the 10,000 numbers problem (see: #3) in Python.

Wait But Why on AI

Do a short course that interests you at DeepLearning.ai

Kwan Ming Mang's slides on Python annotations

Write 2048.

Notes for MTSS

GRT Design To Do List

Assuming you have set up your account, go to ACCOUNTNAME.gunnhigh.school/explore to find your work.

The tutorials are on Qoom.

Group 1 feedback survey

Group 2 feedback survey

Group 3 feedback survey

Group 4 feedback survey

Which group had the best proposal?

Data Science at LinkedIn

10/15--10/18

10/21--10/25

A Declaration of the Independence of Cyberspace

11/4--11/8

Biden Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence

Fact sheet on the Executive Order

UN's High-level Advisory Body on Artificial Intelligence

11/12--11/15

Declare Project

Generative AI transformers (Financial Times article)

Making a GPT

Geoffrey Hinton interview on 60 Minutes

Google BARD v ChatGPT chess match

11/25--11/29

After Thanksgiving Survey

Sabermetrics 101: Introduction to Baseball Analytics

12/9--12/13

Fall Semester Presentation Signup Sheet

ChatGPT passes the AP CS A test

Sabermetrics 101: Introduction to Baseball Analytics

12/16--12/20

Fall Semester Presentation Signup Sheet

If you're interested, a friend of mine has some recommendations about machine learning that may be worth exploring.

Machine Learning Crash Course (Google; 16 hours)

Machine Learning Class (Coursera version from Stanford University; 56 hours, certificate optional and costs money)

Stuff Nikolai likes:

Stanford CS 231N -- the "notes" links are to updated version of original Karpathy notes -- looks quite good still. Ones like "image classification notes" and "backprop notes"
http://cs231n.stanford.edu/syllabus.html

Here is Karpathy's recent blog -- great overview of how to work on deep learning, data driven problems.
http://karpathy.github.io/2019/04/25/recipe/

Lastly I'd make sure to introduce students to SKLearn, Numpy, etc. Great libraries. Also Pandas DataFrames...
https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC
I did mention Kaggle -- have some reservations about recommending it highly. But it's still a good source for many public datasets and ML competitions.

They claim 14,000 datasets, which is something! And lots of public solutions. 
https://www.kaggle.com/datasets

Stuff my fake son-in-law likes:

http://www.fast.ai

12/23--12/27

12/30--1/3

Winter Break

1/8--1/10

ABC News video on design thinking

Stanford students discussing details of design thinking

Design Thinking in a nutshell

At this point, you are either working on your project, or you are finding at least one student who is struggling with a class and understand their needs so you might be able to help them.

4/7--4/11

3Blue1Black: "But what is a GPT? Visual intro to Transformers | Chapter 5, Deep Learning"