Lecture 31
Crash course on machine learning
MCS 275 Spring 2022
Johnny Joyce
Lecture 31: Crash course on machine learning
Course bulletins:
- Watch video for lecture 30
- No in-person meeting on Friday - watch video for lecture 32 by end of Friday.
- Professor Dumas returns on Monday 4 April
Before I say anything...
What does the phrase "machine learning" make you think of?
Contents
- What is machine learning?
- What are some examples of machine learning?
- How can you get into machine learning?
What is machine learning?
"The study of computer algorithms that can improve automatically through experience and by the use of data." - Wikipedia
Motivation: Dog or cat?
How do we write a program to find out whether an image of is a dog or a cat?
Main types of machine learning:
Quick examples (unsupervised):
Quick examples (unsupervised):
Quick examples (unsupervised):
Quick examples (supervised):
Classification — Assigning labels to data with labeled categories. E.g. is an image a dog or a cat?
Regression — Assigning labels to data with continuous labels. If we know a student's midterm scores, can we predict their final scores?
Let's define something called a "neuron":
Neural networks
They are a huge collection of "neurons", inspired by the brain.
Loss function — Characterizes how "far" our model's predictions are from the true answers
How can you do machine learning with Python?
- scikit-learn (simplest) — Good for general machine learning or quick implementations.
- PyTorch — Good for deep learning.
- TensorFlow — Good for deep learning.
What tools help with machine learning?
- Python programming
- Linear algebra
- Calculus
- Statistics
What tools help with machine learning?
- Python programming ✅
- Linear algebra
- Calculus
- Statistics
Classes at UIC relating to machine learning
- MCS 548 — Mathematical Theory of Artificial Intelligence,
- CS 411 — Artificial Intelligence I,
- CS 412 — Introduction to Machine Learning
Summary
- We saw the main types of machine learning (supervised, unsupervised, and reinforcement)
- We saw brief examples of supervised/unsupervised learning
- We saw neural networks and built one in scikit-learn
Interesting related links