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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 Rule-Based Learning. The main focus of the course will be on Neural Nets, Genetic Algorithms and Reinforcement Learning.

Course Objectives

  1. To learn the basic concepts behind machine learning/intelligence
  2. To learn different meta-heuristics for function approximation
  3. To learn how to choose the right learning technique for a given problem
  4. To learn the difference between shallow and deep learning
  5. To learn how to verify the learning capabilities of a given technique via proper theoretical and experimental tools
  6. To learn how to run experiments and validate/compare algorithms
  7. To learn how to write a scientific paper
Week Topic Tutorial
1 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 Dealing with Data, Encoding and Experiments
Data Compression: PCA and t-SNE, Fisher Vector

Basics of statistics
3 Dealing with Data, Encoding and Experiments
VLAD, Other encoding methods, K-Fold Cross Validation, Leave-One-Out

PCA and t-SNE
4 Classification and Clustering
K-Means and FCM, Support Vector Machines

Validation
5 Classification and Clustering
Support Vector Machines, Self-Organizing Maps

K-means versus SVM
6 Learning
Perceptrons, MLPs and Backpropagation algorithms

SOM
7 Learning
Deep Learning: autoencoders and Convolutional Neural Networks (CNNs)

MLP
8 Learning
Reinforcement Agents
CNN
9 Uncertain and Vague Knowledge
Evolving Fuzzy Inference Systems, Decision Trees, Random Forests

Autoencoders
10 Uncertain and Vague Knowledge
Probabilistic Methods: Naive Bayesian, Hidden Markov Models

Decision trees & random forests
11 Evolution and Animals
Genetic / Evolutionary Algorithms and Differential Evolution, Ant Colonies and Particle Swarms

Naïve Bayesian
12 Ethics of Machine Learning
Ethics and Philosophy, Ethics and Social Consciousness
How to write a scientific paper?

H.R.Tizhoosh Personal Website