AIBridge is an introductory AI bootcamp developed and taught by Prof. Xin Liu, yours truly, and Samuel Ren in collaboration with AIFS.

AIBRidge Notes

• Pause [more] to allow some time to see if people follow
• did y’all not introduce pandas?

Closest to doing this without try/except:

• slide 49: what is conc?
• is this too much recap time? Haven’t we been recapping for a long while already?
• probably good to mention what is /content/iris.data, also, just opening from ./iris.data should work and will be probably more ergonomic
• read function confusion
  • .read() => str
  • .readlines() => [str]
  • the pauses feel a tad ackward?
  • speak up!

• SSE squares and lines need to be darker: increase opacity 39
• “very common metric” — not a metric
• motivate confidence value better; the “middle” question makes sense
• I think its actually probably good to explain cross-entropy in the future
  • (i.e. its not a lot of fancy math + I think it provides a lot of intuition w.r.t. one-hot encoding, probablitiy distributions, etc.)
• Problem with how I made the old slides: multi-Class classification (1va, ava, etc.) needs better motivation before, otherwise throwing three classes on the screen is a tad confusing
• motivate that the whole random.seed business is so that the whole class can compare answers more effectively
• LogReg = LogisticRegression(), typically, name instance variables as lower snake case; so maybe call it my_log_reg or something

Welcome to the AIBridge Course homepage.

The purpose of AIBridge is to bridge the gap between computer science and other disciplines. To many, working with AI might seem like an unreachable objective. However, in reality, one week is enough to get started. AIBridge will provide basic programming capability in Python and knowledge of object-oriented programming as well as the concepts behind machine learning and how to implement it using a popular toolbox, Scikit-Learn. Students work to complete a personally-defined project using techniques in AI, with data from their own research or with problems supplied by the Course. This one week course will be hosted in-person at UC Davis and will target mainly undergraduate and non-technical graduate students.

Part 1: ML Training Practice

One of the things that makes a very good Sommelier is their ability to figure out as much details about a wine as possible with very little information.

You are tasked with making a Sommelier program that is able to figure both the type and quality of wine from available chemical information. Also, you have a “flavor-ater” machine that makes a linear combination of multiple chemical features together (similar to PCA), which is counted as one chemical feature after combination.

SPOILER ALERT for future labs!! Don’t scroll down!

We are going to create a copy of the iris dataset with a random variance.

import sklearn
from sklearn.datasets import load_iris

Let’s load the iris dataset:

x,y = load_iris(return_X_y=True)

Because we need to generate a lot of random data, let’s import random

import random

Put this in a df

import pandas as pd
df = pd.DataFrame(x)
df

       0    1    2    3
0    5.1  3.5  1.4  0.2
1    4.9  3.0  1.4  0.2
2    4.7  3.2  1.3  0.2
3    4.6  3.1  1.5  0.2
4    5.0  3.6  1.4  0.2
..   ...  ...  ...  ...
145  6.7  3.0  5.2  2.3
146  6.3  2.5  5.0  1.9
147  6.5  3.0  5.2  2.0
148  6.2  3.4  5.4  2.3
149  5.9  3.0  5.1  1.8

[150 rows x 4 columns]

Let’s make 150 random numbers with pretty low variance:

This is usually not needed if you are using Google Colab. If you are following the instructions provided during our lecture series, please disregard this page.

However, students have expressed interest in working with their own system’s copy of Jupyter or local installation. We therefore provide a set of very tenuous instructions for installing the tools used in our session using vanilla C-Python (i.e. not anaconda/conda/miniconda.)

Python

Our tools target Python 3.8+. Use your system’s package manager to install Python at least version 3.8, or use Python Foundation’s universal installers.