AI From Scratch

AI from Scratch is a lecture series run by Hessian AI Labs in association with the AI for Society (AIFS), Sydney chapter. The goal is to up-skill or re-skill the workforce to empower them with the capability to leverage the power of data and AI.

Lectures:

• Wed, 9:15-10:30pm AEST
• Sun, 10-12am AEST

Google Hangouts:

If you're interested in joining the sessions, drop me a line at [email protected]

This is a long running course that is focused on building up concepts and ideas, including the necessary math, from scratch. Initially, we'll focus on building the intuition for Machine Learning while developing the math in parallel. Course structure is as follows:

1. Machine Learning Basics
   • Introduction to Learning Algorithms
   • Foundational ML concepts
2. Machine Learning Algorithms
   • Linear Models
   • Non-linear Models (Neural Networks)
   • Kernel Methods
   • Graphical Models
   • Latent Variable Models
   • Approximate Inference and Sampling
   • Dimensionality Reduction
3. Deep Learning
   • Visual data based models
     • CNN etc.
   • Sequential data based models
     • RNN etc.
   • Generative models
     • VAE, GAN etc.

Recommended reading material:

Math

• Introduction to Linear Algebra, Gilbert Strang
• All of Statistics, Larry Wasserman
• Deep Learning, Ian Goodfellow et. al. (Ch2-4)

ML

• Pattern Recognition and Machine Learning Christopher M. Bishop
• Deep Learning, Ian Goodfellow et. al. (Ch5)

DL

• Deep Learning, Ian Goodfellow et. al.