Teaching …
SYDE522  Machine Intelligence
Course Description
The objective of this course is to introduce the students to current
machine learning concepts. An overview of different learning schemes will be provided, including:
Decision Tree, Bayesian, Inductive, Analytical and RuleBased Learning.
The main focus of the course will be on Neural Nets, Genetic Algorithms
and Reinforcement Learning.
Course Topics
Week 
Topic 
Tutorial 
1. Week 
Introduction
What is Intelligence? A bit on Terminology
A Brief History of MI/ML
Features, perceptrons, probabilities, sets and statistics 
Potential projects and datasets 
2. Week 
Dealing with Data, Encoding and Experiments
Data Compression: PCA and tSNE, Fisher Vector 
Basics of statistics 
3. Week 
Dealing with Data, Encoding and Experiments
VLAD, Other encoding methods, KFold Cross Validation, LeaveOneOut 
PCA and tSNE 
4. Week 
Classification and Clustering
KMeans and FCM, Support Vector Machines 
Validation 
5. Week 
Classification and Clustering
Support Vector Machines, SelfOrganizing Maps 
Kmeans versus SVM 
6. Week 
Learning
Perceptrons, MLPs and Backpropagation algorithms 
SOM 
7. Week 
Learning
Deep Learning: autoencoders and Convolutional Neural Networks (CNNs) 
MLP 
8. Week 
Learning
Reinforcement Agents 
CNN 
9. Week 
Uncertain and Vague Knowledge
Evolving Fuzzy Inference Systems, Decision Trees, Random Forests 
Autoencoders 
10. Week 
Uncertain and Vague Knowledge
Probabilistic Methods: Naive Bayesian, Hidden Markov Models 
Decision trees & random forests 
11. Week 
Evolution and Animals
Genetic / Evolutionary Algorithms and Differential Evolution, Ant Colonies and Particle Swarms 
Naïve Bayesian 
12. Week 
Ethics of Machine Learning
Ethics and Philosophy, Ethics and Social Consciousness 
How to write a scientific paper? 
Course Objectives

To learn the basic concepts behind machine learning/intelligence

To learn different metaheuristics for function approximation

To learn how to choose the right learning technique for a given problem

To learn the difference between shallow and deep learning

To learn how to verify the learning capabilities of a given technique via proper theoretical and experimental tools

To learn how to run experiments and validate/compare algorithms

To learn how to write a scientific paper
All information related to this course is available on LEARN:
http://learn.uwaterloo.ca
