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Machine Learning Class

Machine Learning applied to Planetary Sciences

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We will focus in the study of Machine Learning techniques, and their applications. Emphasis will be on geological, planetary and astronomical applications. Course will cover basic and state of the art techniques, as well as validation techniques for each algorithm.


Students will be able to implement and validate different Machine Learning techniques in datasets related to Planetary Sciences, Earth Sciences and Astronomy. Students will learn the difference between supervised and unsupervised learning.

Course Structure

Lecture Times

Two lectures will be held per week on Mondays and Wednesdays from 10:00 AM to 10:50 AM. Both lectures will be in room 312 in the Kuiper Space Sciences Building.

Course Website

Lectures, homework assignments and general information on the course will be available online in the following website:

Office Hours

I will be available for questions and discussions after lectures. I will have office hours from 13:00 – 14:00 on Tuesdays and Thursdays. Otherwise, the students can make an appointment.


The evaluation will consist of:

There will be an additional 10% extra credit opportunity by submitting additional reports, extracurricular projects and extra participation during the classes.

Homework assignments submitted after the established due date will be accepted. Assignments submitted after the due date will have a penalty of 20%.


Final project: 60%

In-class participation: 40%

Grades are assigned as follows:

Absence Policies

Attendance to normal sessions is not mandatory.

In case of extraordinary circumstances and the students miss either of the quizzes; there will be opportunity for an additional quiz. The instructor will set the date of the make-up quiz. There will be only a single date for additional quizzes.

Circumstances that merit an additional quiz are:

Number of required examinations and papers

For Graduate students, there will be two quizzes, four assignments and one final project.

For Undergraduate students, there will be two quizzes, four assignments and one final project.


  1. Introduction (Week 1)
  2. What is Machine Learning?
  3. Modern applications of Machine Learning.
  4. Python-Pandas Introduction.
  5. Fundamentals (Week 1-2, 1 assignment)
  6. Matrix Algebra
  7. Probability Distributions
  8. Supervised Learning Techniques. (Week 3-6, 1 assignments, 1 quiz)
  9. Regression First Example Second Example Data Science
  10. Classification 1. Logistic Regression. 2. Support Vector Machines. 3. Artificial Neural Networks 4. Deep Learning 5. Convolutional Neural Networks Convolutions Example
    1. TensorFlow Classifier
    2. TensorFlow CNN
  11. Validation Methods (Week 7-8, 1 assignment)
  12. Cross Validation
  13. Bias – Variance Analysis
  14. [ROC and AUC] (
  15. Supervised Learning within the context of Planetary Science (Week 9-10)
  16. Unsupervised Learning Techniques (Week 11-15, 1 assignment, 1 quiz)
  17. Clustering 1. K-Means 2. Applications
  18. Dimensionality Reduction techniques 1. (Principal Component Analysis)[] 2. Independent Component Analysis 3. T-SNE
  19. Unsupervised Learning within the context of Planetary Science (Week 15-16)
  20. Final Project Presentations (Week 17)




Final Projects

QA Site

Required Texts

The course has no mandatory text and is self-contained; however, the following books are helpful resources for students:

Special Material required for class

The students will need access to a Personal Computer with the Matlab Scientific Program or the Python Programming environment


The students will be able to use pagers/cellphones in the classroom as long as they set them up in silence mode.

Academic Integrity Policy

Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See

Threatening behaviour Policy

Threatening Behaviour is prohibited.

The UA Threatening Behaviour by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself. See

Accessibility and Accommodations

It is the University’s goal that learning experiences be as accessible as possible. If you anticipate or experience physical or academic barriers based on disability or pregnancy, please let me know immediately so that we can discuss options. You are also welcome to contact Disability Resources (520-621-3268) to establish reasonable accommodations.

Please be aware that the accessible table and chairs in this room should remain available for students who find that standard classroom seating is not usable.

Notification on offensive content

Some of the datasets used to exemplify Machine Learning algorithms might content topics like rape, murder, deaths by cancer, diabetes and other illnesses. UA Nondiscrimination and Anti-harassment Policy The University is committed to creating and maintaining an environment free of discrimination; see Our classroom is a place where everyone is encouraged to express well-formed opinions and their reasons for those opinions. We also want to create a tolerant and open environment where such opinions can be expressed without resorting to bullying or discrimination of others.


The information contained in this syllabus other than grade and absence policies may be subject to change with reasonable advance notice.