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
- To learn the basic concepts behind machine learning/intelligence
- To learn different meta-heuristics 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
| 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? |