Introduction to Machine Learning Neural Networks and Fuzzy Systems Course

1. Neural Networks and
Fuzzy Systems
Introduction to Machine Learning
1
Dr. Tamer Ahmed Farrag
Electrical Engineering Department
Taif University
Course No.: 803522-3

2. Course Outline
Part I : Neural Networks (11 weeks)
• Introduction to Machine Learning
• Fundamental Concepts of Artificial Neural Networks
(ANN)
• Single layer Perception Classifier
• Multi-layer Feed forward Networks
• Single layer FeedBack Networks
• Unsupervised learning
Part II : Fuzzy Systems (4 weeks)
• Fuzzy set theory
• Fuzzy Systems
2

3. Introduction to Artificial Intelligence Systems
• Intelligence
• Characteristics associated with intelligence in
human behavior
• Tasks related to intelligence
• Learning
• Intuition
• Creativity
• Inference
3

4. Machine Learning Problems and Learning
Techniques
4
Learning
Problems
Learning
Techniques
Machine
Learning
Supervised
Learning
Classification
Regression
Unsupervised
Learning
Clustering
Reinforcement
Learning
Clustering

5. Learning Methods
Supervised
Learning
Compare
computed output
to correct output
Change network
parameters
accordingly
Unsupervised
Learning
Correct output is
unknown
Adapt to
structural
features of input
patterns
Reinforced
Learning
Can only tell if
output is
incorrect
Learn by making
mistakes
5

6. Supervised Learning
6https://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article
• The training process required to make predictions and is corrected when those
predictions are wrong.
• The training process continues until the model achieves a desired level of
accuracy on the training data.
• Example problems are classification and regression.
• Example algorithms include Logistic Regression and the Back Propagation
Neural Network.

7. Unsupervised Learning
7https://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article
• A model is prepared by deducing structures present in the input data. This
may be to extract general rules. It may be through a mathematical process to
systematically reduce redundancy, or it may be to organize data by similarity.
• Example problems are clustering, dimensionality reduction and association
rule learning.
• Example algorithms include: the Apriori algorithm and k-Means.

8. Reinforcement Learning
(semi-supervised learning)
8https://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article

9. Types of Machine Learning Models
(problems and tasks)
9
Classification
• A classification problem is a problem where we are using data to predict which
category something falls into.
• we are trying to use data to make a prediction about a discrete set of values or
categorizes.
• An example of a classification problem could be analyzing a image to determine if it
contains a car or a person.
• It uses supervised learning methods.
Regression (Prediction)
• Regression problems are problems where we try to make a prediction on a continuous
scale.
• Examples could be predicting the stock price of a company or predicting the
temperature tomorrow based on historical data.
• It uses supervised learning methods.
Clustering (Grouping)
• Clustering is a process of grouping the data into classes and clusters where the
objects reside inside a cluster will have high similarity
• The main target of clustering is to divide the whole data into multiple clusters.
• Unlike classification process, here the class labels of objects are not known before,
• It using unsupervised learning methods.

10. Sample of Machine Learning Technologies
• Neural Networks
• Learn the relationship between input and output by example.
• Fuzzy Logic
• Use probability to represent uncertain facts and apply logical
reasoning to partial truths.
• Genetic Algorithms
• Evolve a solution by repeatedly mixing possible solutions and
selecting the solution that leads to the best results.
• Expert Systems
• Use a pre-existing knowledge base to evaluate input and
make informed decisions.
• Probabilistic Reasoning
• Use statistics to make decisions or predict an outcome.
10

11. Machine Learning vs Deep Learning
11