Elective Course: Introduction to Machine Learning in R

5 lectures of 60/90 minutes:

1. Introduction to R and the topics. Basic commands and mini-test

2. Data pre-processing. Exploratory Data

3. Models and performance measures. Case study

4. Other models in ML and introduction to Markdown

5. Capstone projects and next step

1. Hands on in R: Exercises and project.

2. Understand the machine learning project pipe

3. Introduce useful models for classification purposes

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https://blogs.sas.com/content/subconsciousmusings/2013/01/11/why-people-and-process-matter-in-addition-to-great-technology-in-predictive-analytics/#prettyPhoto

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1. Why R ? Open source vs Commercial softwares.

2. What is Machine learning, AI , Deep learning and Reinforcement Learning?

3. Data : Revolution ( “the new energy/oil”) or a buzzword ?

1. Why R ? Open source vs Commercial Statistical softwares.

• Reproductibility in the project

• Free / Open Source -> big community and free resources availables

• Easier automation and more powerful advanced process

• Better reading different data sources and capable of processing big data

• Runs on a wide array of platforms and likely to run on any computer

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