Lectures

Week num Topic Resources
1 1 Introduction PDF
  2 Search 1 PDF
    Tutorial 1 --- AI History Tutorial 01
2 3 Search 2 PDF
  4 Machine Learning 1: Basics, types, paradigms, training set vs test set, generalisation PDF
    Tutorial 2 Tutorial 02
3 5 Machine Learning 2: K-Nearest Neighbour and K-Means and K-fold Cross Validation PPT, PDF
  6 Machine Learning 3: Decision tree learning method PPT, PDF
    Tutorial 3 Tutorial 03
4 7 Neural Network 1: Perceptron learning PPT, PDF
  8 Neural Network 2: Back Propogation PPT, PDF
    Tutorial 4 Tutorial 04
5 9 Neural Network 3: Neural Engineering PPT, PDF
  10 Evolutionary Computing 1: Evolutionary Computing and Learning PPT, PDF
    Tutorial 5 Tutorial 05
6 11 Evolutionary Computing 2: From Genetic Algorithms to Genetic Programming PPT, PDF
  12 Evolutionary Computing 3: Genetic Programming for Regression and Classification PPT, PDF
    Tutorial 6 Tutorial 06
Trimester Break
7 13 Reasoning under Uncertainty Basics PPT, PDF
  14 Bayes theorem and Classification by "Naive Bayes" PPT, PDF
    Tutorial 7 PDF
8 15 Introduction to Baysian Networks PPT, PDF
  16 Probabilities and how to build a Baysian Network PPT, PDF
    Tutorial 8 PDF
9 17 Inference in Baysian Networks (Part 1) PPT, PDF
  18 Inference in Baysian Networks (Part 2) PPT, PDF
    Tutorial 9: Baysian Networks PDF
10 19 Planning and Scheduling 1 -- Classical Planning PDF
  20 Planning and Scheduling 2 -- From Planning to Scheduling PDF
    Tutorial 10 PDF
11 21 Planning and Scheduling 3 -- Dynamic Scheduling PDF
  22 Planning and scheduling 4 -- Routing PDF
    Tutorial 11 PDF
12 23 3O --- Overview, Other Topics and Other Information (Part 1: Knowledge based systems, Natural language processing, Data mining and web mining) PDF
  24 3O --- Overview, Other Topics and Other Information (Part 2: Big Data, Deep Learning, Further AI Courses, Scholarships, etc.) PDF
  Summary 3O --- Overview, Other Topics and Other Information (Part 3: Other Information: AI Projects, Summary and Exam) PDF