6 Discussion assignments – average will be 35% of your grade

6 Project assignments – average will be 35% of your grade

3 Semester Exams -- average will be 30% of your grade

An overall grade will be assigned based on the following scale:

90% - 100% A 89% - 80% B 79% - 70% C 69% - 60% D 0% - 59% F

Textbook

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author Géron, Aurélien, author.
Edition 3rd edition
Publication Information O'Reilly Media, Inc., 2023.
Publication Date 2023
View Now: O'Reilly for Higher Education
Format: Electronic Resources

This content is freely available to students thanks to ACC subscription to O’Reilly for Higher Education 

Topics covered:

1. Introduction to Machine Learning

2. Types of Machine Learning Systems

3. Main Challenges of Machine Learning

4. Testing and Validating

5. End-to-End Machine Learning Cycle

6. Classification

7. Regression

8. Support Vector Machines

9. Decision Trees

10. Ensemble Learning and Random Forests

11. Dimensionality Reduction

12. Unsupervised Learning

By the end of this course, the student will be able to:

1. Explain key principles of machine learning (ML).

2. Compare and contrast machine learning paradigms and their applications.

3. Implement end-to-end machine learning process using Python libraries.

4. Evaluate quality of machine learning model by utilizing different performance metrics

5. Compare differences in interpretability of learned models and impact of decisions arising from results.

In 1989, the U.S. Department of Labor education jointly surveyed U.S. employers to find out the most important skills and competencies needed by workers. The results of that survey identified SCANS (Secretaries Commission on Achieving Necessary Skills). These are skills that employers need the most from their workers. SCANS skills are the predictors of success in workplace. The following list summarizes the SCANS competencies addressed in this course:

For expanded definitions of the listed SCANS, please go to: www.academicinnovations.com/report.html

Professor Ralph E. Hooper

Office Phone: 512-223-2599

Office Location: Room 1300.25 San Gabriel Campus 

Virtual Office Hours: Mon & Wed 1:00 pm -- 3:30 pm via Zoom (email for appt.)

ACC email: ralph.hooper@austincc.edu -- Zoom will be available for meetings

Instructor Website: https://hooper.accprofessors.com/ 

Instructor Bio: I have been teaching at the college level for over 35 years in both mathematics and computer science. My research interests are computational thinking and educational technology. I enjoy travel and baseball.