Basics of Machine Learning

Overview

Teaching: 25 min
Exercises: 120 min

Questions

• What type of research problems can machine learning solve, and what approach do models use to accomplish this?

• How does a machine learning model make predictions from its inputs?

• What are a set of key steps (also called a workflow) for solving a machine learning research problem?

Objectives

• Students can describe the structure of several basic machine learning model types, the types of predictions that can be made, and metrics for assessing regression and classification performance

• Students will practice using jupyter notebooks to execute machine learning workflows on cloud computing platforms which they can transfer to other projects.

• Students will assess model performance on two potential applications and justify which task the model is appropriate for

Background

In this introductory lecture lecture we give a summary of basic machine learning model types, descriptions of input features, data types, classes of models, and a description of several overarching ideas in machine learning.

Recorded Lecture

You may also view the Lecture Slides and Lecture Notes in addition to the recorded lecture.

Readings

Here are a few nice introductory articles discussing machine learning basics. They cover the topic from slightly different perspectives, and go into some concepts which will be covered in more detail in other modules.

Towards Data Science Article

Machine Learning Mastery Article

Activity: Introduction to Machine Learning for Materials Science

This activity will give a hands on example of executing a machine learning workflow for training a model to predict materials properties. The activity runs through a Jupyter Notebook hosted on the Nanohub platform. This notebook contains instructions, code, and exercises to read, execute, and answer within the notebook itself. You can begin the activity by Launching the tool on Nanohub Here. In order to complete this activity you will need to either create an account or log in with an existing google account to be able to run the tool.

If you’d like you may also follow along with a video walkthrough which discusses the main sections of the notebook activity.

Important!
Saving your work in Nanohub is a bit tricky. Follow these instructions to create a copy of the notebook once you’ve launched the tool via the link.

1. With your notebook open (by clicking the launch tool button) make a copy if you haven’t already using the File drop down menu in the top left.
2. The copy will open in a new tab in your browser. Save the copy by clicking “save and checkpoint” under the File drop down menu in the new tab.
3. At this point you may close the original tab as we won’t use it anymore.
4. To find the newly copied file (to download or open it up later) we will use the Jupyter tool on Nanohub. Launch this tool which will give us access to the virtual computer that Nanohub is hosting for us.
5. Navigate to the folder “data/results/####/intromllab/bin/”. Note: the number will be a unique number associated with your session. Pick the most recent one if there are multiple (or an older one if you are looking for an older saved file).
6. Double click the notebook file “intromllab-Copy1.ipynb” to launch your saved notebook. This is the newly copied notebook file that you just created.
7. You can now save this notebook freely, and to return to this notebook later launch the same tool as step 2 and follow the instructions from there.

Note: do not move the notebook or save to another location on nanohub’s virtual computer or else some aspects of the code will break.

Follow the Link to get started if you did not click above.

Extra Help

If you are starting with little python backgroud you may find it useful to review the introductory module on python programming via software carpentry.

Key Points

• Machine learning (specifically supervised learning) can be used to model complex materials properties that are hard to obtain experimentally

• Machine learning models make predictions by learning relationships and patterns from existing data, and use those learned patterns to make predictions of properties of new materials

• A workflow for building machine learning models can be broken down into key steps: Data Cleaning, Feature Generation, Feature Engineering, Model Assessment, Model Optimization, and Model Predictions