Our Very First Event: Foundations of AI

18 July 2020;

Just 2 weeks back, we kicked off our very first live AI event, titled: Basics of AI!

GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials

A Public Repository containing all of NYP AI’s past event materials - GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials

GitHubNYP-AI

We touched upon the 3 learning paradigms of Machine Learning (Supervised, Unsupervised & Reinforcement Learning) and their real world applications.

A 7 step framework for Machine Learning projects was also explained such that we can undertake AI projects efficiently. With a live Q&A session at the end of each section, members were able to clarify any doubts they had.

7 Step Framework

After learning about the fundamentals of AI, we dived right into one of the most popular Supervised Learning Algorithms: Linear Regression. Coupled with our very own Colab python notebooks and slides, our members were able to understand this ML algorithm. We utilized the python libraries: Pandas, Matplotlib, Numpy and Scikit-learn in our notebooks, which allowed us to visualize and implement our algorithms smoothly and swiftly.

Using python ML Libraries

7 Step Framework

To consolidate our newly-gained knowledge, we had a 30 minutes lab session where we had to utilize the California Housing Dataset to predict housing prices, using the Linear Regression Algorithm.

California Housing Dataset

In this lab session, we utilized the 7 step framework which allowed for a systematic workflow. Members were able to put into action hyperparameter tuning, as well as data preprocessing.

After training the model, they were able to see the prediction capabilities of their model by feeding it unseen samples.

Wrapping things up, we had a live walkthrough of the Python notebook, explaining our solution to our members.

We look forward to hosting our very next event, next semester! Until then ~