Machine Learning and AI, concepts and techniques (Lecturer Notes)

In-class practices

The lecturer should start with a presentation about AI concepts (e.g., AI, Machine Learning, Types of ML techniques, Deep Learning, and state of the art techniques in Artificial Intelligence) based on the CyBOK Knowledge Areas materials and your own teaching slides. Next, follow the below steps:

Play one or more videos introducing Machine Learning

video 1: Introduction to Machine Learning ( MIT OpenCourseWare)

video 2: Introduction to Deep Learning ( 3Blue1Brown )

A. Students build simple machine learning to understand the steps of creating an ML. There is no need to understand all the codes. It is essential to understand how an ML works and compare some algorithms. They should learn supervised and unsupervised machine learning algorithms.

The code is available here. Students should read the descriptions, add codes, run them, and get the expected result for every section.

The lecturer should explain each part and show students the expected result. Students can do this in small groups. If they need to understand/find a Python command, they can use the following references. The lecturer should explain the goal (i.e., the purpose of building the machine learning models, which are explained in the lecturer note)

Ask students to implement the notebook for introduction to machine learning:

Dataset:

__________________________________________________

Code Explainer video: