Dr. Khalil Chebil

Algorithm analysis, recursion, lists, stacks, queues, trees and graphs, with Java labs.

Brute force, divide and conquer, transform and conquer, dynamic programming and greedy algorithms.

Agents, search, logic, machine learning and knowledge graphs, taught from the interactive textbook.

Describing datasets, probability and inference, up to ANOVA and Bayesian methods.

Deductive reasoning, machine learning and integration: agents, search, logic, neural networks, reinforcement learning and knowledge graphs. Every chapter pairs theory with an implementation notebook and a lab.

Summarising and plotting data, random variables and distributions, confidence intervals, significance, ANOVA and Bayesian inference.

One small project, from its first commit to a published Docker image: pull requests, project boards, GitHub Actions and Docker.

The lecture decks of the Advanced Artificial Intelligence course.