Machine Learning with Python

Speaker: Yingzhi Gou

Decision Systems Lab,

University of Wollongong

NOTE this jupyter notebook is available on github https://github.com/YingzhiGou/AI-Meetup-Decision-Systems-Lab-UOW

Acknowledgement

this tutorial is based on the book Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

What is machine learning?

"A computer program is said to learn from experience $E$ with respect to some task $T$ and some performance measure $P$, if its performance on $T$, as measured by $P$, improves with experinece $E$" ---- Tom Mitchell, 1997

Types of Machine Learning

With or without human supervision?

• Supervised Learning (classification, prediction)
• Unsupervised Learning (clustering, visualization, dimensionality reduction, association rule mining, anomaly dection)
• Semi-supervised Learning
• Reinforcement Learning (e.g. to learn the policy that has the highest award)

Can or cannot learn incrementally on the flay?

• online learning
• batch learning

Comapring with known data or with a abstracted model of known data?

• instance-based learning
• model-based learning

Challenges of Machine Learning

• Insufficient Quantity of Training Data
• Nonrepresentative Training Data
• Poor-Quality Data
• Irrelevant Features
• Overfitting the Training Data
• Underfitting the Training Data
• and more