**Abstract:** Machine Learning involves statistical techniques and intelligent algorithms that allow the learning stage to generate structures based on the training data in order to predict results for the new data in the testing stage. Learning stage involves optimizing a numerical measure to compute the parameters that characterize a given algorithm's underlying model. Existing machine learning techniques are expressed as supervised or unsupervised. Machine Learning techniques have evolved over the last decade and have been useful in different domains. R is one of the well-known Statistical and Machine Learning software. This workshop will show how to apply different Machine Learning Algorithms using R. **Resource Person:** Dr. Soud Larabi, Ph.D. Artificial Intelligence

2.An Introductory Workshop on Data Analytics for Lean Six Sigma.

**Abstract:** Six Sigma is a data driven methodology to improve the quality of a process (i.e. any repetitive business function) by reducing the variation around the mean of the process. In other words, it ensures that the process falls within the acceptable tolerance range (as far as possible). In theory, a perfect score in Six Sigma is 6. This would mean that 99.7% of all data points fall within the tolerance range. However, in practice, a good sigma score depends upon the dynamics of the particular process in question. At the end of this workshop the participants would be able to understand the basic data analysis and interpretation of the data gathered within a DMAIC project. The emphasis will be on use of data analytics tools and the interpretation of the outcome. Interactive activities and many different examples from actual Lean Six Sigma projects will be used to illustrate all tools.

**Hour 1:** Data and Lean Six Sigma **Hour 2:** Introduction to testing **Hour 3:** Regression Analysis
**Resource Person:** Saima Rashid, Director Center for Statistics and Information, Six Sigma Black Belt Certified Professional

3.Data Analysis using Python

** Abstract:**This workshop covers the fundamental concepts of object oriented programming using python language such as primitive data types, arithmetic and logical operators, user interaction, control structures, functions and classes. In addition, it also introduces the collections such as strings, lists, dictionaries, tuples, and sets and their effective use in variety of applications. It also covers the basics of higher order data structures and data analysis tools like numpy and pandas. The participants will be exposed to various data analysis tools using Python. Participants will also gain hands-on experience with tools related to data analysis as part of their exercises/small project during the workshop.

**Hour 1: Introduction to Python**

1. Python Environment Setup and Jupyter Notebook.

2. Introduction to Python Programming (primitive data types, arithmetic and logical operators, user interaction, control structures, functions and classes. In addition, it also introduces the collections such as strings, lists, dictionaries, tuples, and sets).

**Hour 2: Python Data Structures**

1. Collections: strings, lists, dictionaries, tuples, and sets.

2. Numpy Arrays: The data scientist data structure for data analysis.

3. Pandas: The database-like data structure for data analysis.

**Hour 3: Python Data Analysis**

1. Project: Small Data Analysis project using Numpy Arrays and Pandas.

2. Project Evaluation and Conclusion.

**Resource Person:**Dr. Anees Araa, PhD Computer Science

4.Deep Learning with Tensor flow.

** Abstract:**Deep learning is an emerging subfield in machine learning that has in recent years achieved state-of-the-art performance in a range of tasks such as image classification, object detection, self-driving cars, segmentation, etc. This workshop will introduce the basic theory of Deep Learning in the first part. For the second part, a prepared hands-on example will be provided for the participants to practice both feed-forward networks and convolutional networks.

We will use the famous MNIST Dataset to build two Neural Networks capable of performing handwritten digits classification. The first Network is a simple Multi-layer Perceptron (MLP) and the second one is a Convolutional Neural Network (CNN). When given an input our algorithm will return the digit this input represents.

** Workshop outline: **

**Hour 1: Classify MNIST using a simple model. **

a. Evaluating the final results.

b. How to improve the model?

**Hour 2: Deep learning applied on MNIST.**

a. Summary of the Deep CNN.

b. Define functions and train the model.

d. Evaluate the model.

**Prerequisite knowledge: **

o Basics of machine learning.

o Better to have basic knowledge of Python.

o Basic knowledge in programming

**Targeted Audience: **Researchers, engineers, computer programmers, and computer science students.

**Resource Person:**

Eman Albilali, King Saud University.

Nourah Alangari, King Saud University

5. Machine Learning in Healthcare using Matlab

** Abstract:**Although the aptitude to record massive amounts of information about individual patients is transmuting the healthcare industry, the volume of data being gathered is impossible for human beings to evaluate. Machine Learning (ML) paves a pathway to automatically find patterns and inspect unstructured data. This permits healthcare professionals to transfer to a personalized care system, which is known as precision medicine.

This talk will highlight the use of machine learning and Artificial Intelligence in various tracks of medical practice from diagnosis to remote health monitoring.

** Workshop outline: **

- Know the state of the art machine learning algorithms.

- Identify various types and categories associated with ML (supervised Vs unsupervised).

- Define and identify appropriate Data Science and Machine Learning Methods per application.

- Practice of machine learning algorithms for given problem to come up with desired output.

**Prerequisite knowledge: **

- Basics of machine learning.

- Basic knowledge in programming.

**Targeted Audience: ** Researchers, engineers, computer programmers, and computer science students.

**Resource Person:**

Eng. Mai Ali, Instructor of Electrical Engineering at AlFaisal University, Riyadh.
**Email: **mali@alfaisal.edu