Introduction to Machine Learning

Introduction to Machine Learning - Neural Networks

https://ubc-library-rc.github.io/ml-neural-networks

Land Acknowledgement

UBC Vancouver is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) peoples.

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CCDHHN Certificate

This workshop contributes towards the Canadian Certificate in Digital Humanities.

Learning Objectives

Understand neural networks, and their applications in machine learning.
Train neural network models for real-world datasets.
Interpret and analyze neural network model results.

Pre-workshop setup

GOOGLE COLAB (https://colab.research.google.com/)

For hands-on exercises, we will use [Python](https://www.python.org/) on [Jupyter Notebooks](https://jupyter.org/). You don’t need to have Python installed. Please make sure that you have a [UBC Syzygy](https://ubc.syzygy.ca/) or a [Google Colaboratory](https://colab.research.google.com/) account. (You will need a CWL login to access Syzygy.) hands-on exercises, programming tools and libraries, such as [Python] and [scikit-learn] prior familiarity with Python programming is recommended, we do not study the codes in detail. Note that if you need to keep your data on Canadian servers for ethics or privacy reasons, you shouldn't use Google Colab because the data is stored in the US. However, UBC's Syzygy system can also be used to run notebooks like this, and it might better fulfill your ethics requirements.

What are Artificial Neural Networks?

"A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates."

What are Artificial Neural Networks?

Source: ACSICORP

Components of a Neural Network

Source: hmkcode

Training a Neural Network

Source: Medium

Components of a Neural Network

inputs x weights + bias

Source: hmkcode

Exercise 1: Replace the question mark

Run no.	Neuron A's weight	Error
1	5	0.7
2	6	0.5
3	7	0.3
4	?	0.1

Linear vs Non-linear Data

Source: ResearchGate

Why use Neural Networks over simple ML models?

Source: Stack Exchange

Some other reasons to use Neural Networks

Versatility: Can be applied to a wide variety of tasks.
Learning Capability: Can improve over time with more data.
Complex Decision Boundaries: Can model non-linear and complex relationships in data.

Challenges and limitations

Overfitting: Too specific to training data.
Data Requirements: Lots of data needed.
Computationally Intensive: Requires powerful hardware.
Interpretability: Neural networks can be "black boxes".

Open Jupyter Notebooks

Where to go from here?

Learn math, programming, and software development: Digital Scholarship workshops at UBC Library
Online courses such as Supervised Machine Learning course on Coursera
Youtube Tutorials such as Python Machine Learning Tutorial Series
Find similar examples in Scikit-learn documentation
Join communities such as Kaggle or AMS Data Science Club

Where to go from here?

Learn more about Neural Networks in hmkcode

Future workshops

Title	Series 1
Regression models	Wed, September 23, 2026 (11 am to 1 pm)
Classification and clustering models	Wed, October 7, 2026 (11 am to 1 pm)
Neural networks	Wed, October 21, 2026 (11 am to 1 pm)
Large Language Models	Wed, November 4, 2026 (11 am to 1 pm)

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