1. INTRODUCTION

2. Hello! Nice to meet you Name : Derni Ageng Age : 21
Date of Birth : 15 December 1996
Hobby : Playing Badminton
Topic : Machine learning in IoT
Nationality : Indonesia
Home Town : Cibubur, East Jakarta

3. 1. Machine Learning in IoT
Main Goal: Present a paper, explain how RNN works, and implementation

4. Machine Learning
Internet of Things

5. Overview Machine Learning Deep Learning
Automatic learning ability on systems, through this ability, System can learn and improve from experience without reprogram.
Focuses on development of computer programs that can access data and use it and learn for themselves
To improve the function of the system, if there was a change which it will affect the function of the system
Deep learning is a Technique for implementing Machine Learning
It uses neural networks to learn

6. RNN(Recurrent Neural Network)
Remember the parts of the input and use them to make accurate predictions

7. Machine Learning Methods Unsupervised machine learning algorithms
Semi-supervised machine learning algorithms
Reinforcement machine learning algorithms
Machine Learning
Methods

8. Supervised machine learning algorithms
Learned in the past to new data using labeled examples to predict future events
Starting from known training dataset, the learning algorithm produces and inferred function to make predictions

9. Unsupervised machine learning algorithms
 Used when the information used to train is neither classified nor labeled.
Study how system can infer a function to describe a hidden structure from unlabeled data

10. Semi-supervised machine learning algorithms
between supervised and unsupervised learning,
Able to considerably improve learning accuracy
Chose when the acquired labeled data requires skilled and relevant resources in order to train or learn it

11. Reinforcement machine learning algorithms
Allow machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.
learning method that interacts with its environment by producing actions and discovers errors or rewards
Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning

12. Tools
TensorFlow : Framework created by Google for creating deep learning models
Anaconda