1. FUNDAMENTALS OF MACHINE LEARNING AND DEEP LEARNING
An introduction
Presented by
Dr. Stephen Gbenga Fashoto.
Department of Computer Science,
Faculty of Science & Engineering University of Swaziland.

2. Machine Learning Supporting Disciplines

3. Fundamentals of Machine Learning
Developed from Artificial Intelligence
The first artificial neuron learning is the perceptron by Frank Rosenblatt 1957
Key Elements of Machine Learning
Representation: how to represent knowledge.
Evaluation: the way to evaluate performance using metrics. 
Optimization: it is used to determine the optimal result.
Historical Perspective
Supervised Machine Learning
Unsupervised Machine Learning
Semi - supervised Machine Learning
Reinforcement Machine Learning
Deep Learning

4. Supervised Machine Learning
What is SML
Goal of SML
Classification of SML Problems
Examples of SML Algorithm are :
Linear regression for regression problems
Neural networks for classification and regression problems.
Random forest for classification and regression problems.
Support vector machines for classification problems.
Regression 3D
Classification
Reflection
Reflection

5. Text, Graphics & Pictures

6. Unsupervised Machine Learning
What is UsML
Goal of UsML
Classification of UsML Problems
Examples of UsML Algorithm are :
k-means for clustering problems.
Apriori algorithm for association rule learning problems.
Clustering 3D
Association
Reflection
Reflection

7. Text, Graphics & Pictures

8. Semi supervised Machine Learning
Clustering
What is S-SML
Goal of S-SML 3D
Reflection
Association
Reflection

9. Text, Graphics & Pictures

10. Reinforcement Learning
Regression
What is RL
Goal of RL
Steps in RL
Examples of RL Algorithm are :
Q-Learning
Temporal Difference (TD)
Deep Adversarial Networks 3D
Reflection
Reflection
Classification

11. Deep Learning Algorithms
Regression
What is DL
Goal of DL
Steps in DL
Examples of DL Algorithm are :
Deep Boltzmann Machine (DBM)
Deep Belief Networks (DBN)
Convolutional Neural Network (CNN)
Stacked Auto-Encoders
Recurrent Neural Network(RNN) 3D
Reflection
Reflection
Classification

12. Convolutional Neural Network (CNN)
Regression
Introduction to CNN
Application of CNN 3D
Reflection
Reflection
Classification

13. Performance of Machine Learning Under – fitting Graph
Clustering 3D
Reflection
Association
Reflection

14. Over – fitting Graph
Clustering 3D
Reflection
Association
Reflection

15. Generalization Graph
Clustering 3D
Reflection
Association
Reflection

17. Comparison of Machine Learning and Deep Learning
Data dependencies
Hardware dependencies
Execution time

18. PRACTICAL SESSION ON WEKA 3.8.2
DEMO

19. Thank you for listening