Julia programming for ML

The goal of this course is to give you an introduction to the Julia programming language and its Machine Learning ecosystem.

After taking this class, you should be able to write reproducible, unit-tested Julia code and do Machine Learning research in Julia. 

No knowledge of the Julia programming language is required: this course only assumes knowledge of common programming concepts like for-loops and arrays. Occasionally, differences and similarities to Python will be pointed out. If you don't know Python, you can safely ignore these.

The first half of the course is taught in five weekly sessions of three hours. In each session, two lectures are taught:

• General Information, Installation & Getting Help
• Basics 1: Types, Control-flow & Multiple Dispatch
• Basics 2: Arrays, Linear Algebra
• Plotting & DataFrames
• Basics 3: Data structures and custom types
• Classical Machine Learning
• Automatic Differentiation
• Deep Learning
• Project | Workflows: Scripts, Experiments & Packages
• Project | Profiling & Debugging