AIF 550 - Artificial Intelligence and Machine Learning Combo
Course Description
UM's DCIE proudly participates in ACE Learning Evaluations services. As of July 1st, 2024, our AI/ML Combo Program has received college credit recommendations and/or workplace competency validations from the American Council on Education, which endorses the quality of our program in alignment with education and workforce requirements. Put your learning to work today by registering and successfully completing our AI/ML Combo Program!
Registration for our Artificial Intelligence & Machine Learning Combo Certificate Program offers a $1,120 discount vs. a single level/certificate registration. The Program includes the content of all 3 Certificates in progressive format: Fundamentals, Elite and Grand Master. Learners will explore topics, technology, and skills required in the successful application of AI/ML techniques to support key industry needs and demands. The AI/ML Combo certificate is comprised of 120 instructional hours in total, and it is intended for individuals with an active interest in seeking jobs in the high-demand Artificial Intelligence-related fields. You can call 305-284-8841 or schedule a one-on-one with an enrollment advisor to learn more.
The Program aims to provide learners with a comprehensive coverage of core concepts and technologies for AI/ML while steering perspective talents to potential technique areas. We use an application-driven organization that covers a much broader range of technologies in key application areas and cover mathematical skills and programming tools when needed. This approach allows us to cover exciting technologies earlier in the course while providing rigorous foundations and skills needed for innovative research and development. Learners will establish a comprehensive knowledge structure suitable for web service development, intelligent consumer electronics design and prototyping, related product development and validation, and broader engineering tasks in related AI/ML fields. In addition, we cover two key high-utilization components: a comprehensive coding tool/technique package and a comprehensive mathematical modeling package with theoretical AI/ML framework derivation capacity with a computational simulation/evaluation module, and core material for performing MS/ME/Ph.D. level AI/ML job roles. Learners are also expected to establish a high-precision AI/ML knowledge base beyond the utilization or adaptation of existing toolboxes and reference projects and explore the concepts and methodologies under the "surface" application layer. The latter part of the program focuses on tailoring technology precisely toward commercial developments and academic research while reducing the dependency on pre-existing or general-purpose development, and aims at complete freedom of technical adaptation, which leads to full utilization of the potential of this new and exciting technology, and its application fields, facilitating cutting-edge technology growth and fast-tracked career development.
Course Outline
- Concepts
- Tabular Data Analytics and decision support
- Computer Vision
- Understanding Natural Language
- Coding Intensive AI/ML System Implementation
- Coding for AI/ML
- Math for Data Science
- Deep Learning Mechanisms
- Machine Learning Mechanisms and Algorithms
- Massive Scale AI/ML
- Research, Reasoning and Optimal Decisions
Learner Outcomes
Upon successful completion of the AI/ML Combo Program, learners should be able to demonstrate proficiency in:
- Representing and visualizing data in an effective manner.
- Using features to represent data.
- Preprocessing data before it can be used in AI related tasks.
- Computing the performance metrics of classifiers and comparing them.
- Computing the histogram of an image.
- Transforming an image by converting it to gray scale, reassigning colors, matching a histogram, changing contrast, filtering by different methods, etc.
- Understanding how language is represented and processed in the realm of AI.
- Loading, editing, and saving an image programmatically.
- Understanding the basic principles of artificial neural networks (architecture, training).
- Applying the most common techniques to extract features from a dataset or image.
- Understanding what classification in AI means and how it is achieved.
- Solving prediction problems by applying linear regression.
- Computing approximation and classification errors in prediction models.
- Selecting the most adequate activation functions in AI models.
- Understanding how supervised and unsupervised training works.
- Applying methods to prevent AI models from memorization.
- Using Python language to process data and design, train and test AI models.
- Understanding the mathematical foundations of artificial neural networks.
- Representing and interpreting probabilistic events.
- Using mathematical methods such as PCA for dimensionality reduction.
- Applying statistical tests to accept/reject hypothesis about data.
- Applying the gradient descent algorithm to loss minimization problems.
- Understanding how basic classification algorithms work.
- Applying regularization methods for AI models.
- Handling data imbalance.
- Understanding what autoencoders and compression mechanisms are.
- Applying Bayes' Theorem in predictions problems.
- Modeling classification problems by applying prediction trees.
- Running statistical tests to validate hypothesis about data.
Notes
Career Services
The office of Professional Advancement (OPA) is excited to announce that in a partnership with the TOPPEL Career Center at the University of Miami are now offering to our students and alumni a comprehensive suite of career services available via our career services platform Handshake!
What is Handshake?
Handshake is a career services platform created for students and alumni to use in their career development. It's used by over 200,000 employees, including all Fortune 500 companies! Thousands of internships and job opportunities are posted on Handshake by employers specifically looking to hire students.
All students have a Handshake account that is automatically created for them once they begin their program of study at the U. Once you receive an email notification, all you have to do is log into Handshake to activate your account. Handshake will then recommend certain positions to you based on your profile allowing for easy searching. Comparing and updating your profile will provide Handshake with a better idea of which job interviews/internships listings you may be interested in.
Here are some of the awesome things you can do with Handshake:
- Apply for jobs & internships
- Register for career fairs, workshops, & info sessions
- Upload your resume for an online critique from the Toppel Career Center
- Research thousands of employers
- Network with recruiters and other students for career insights
Library Services
The office of Professional Advancement (OPA) is excited to announce that our students now have access to the Otto G. Richter Library services throughout the duration of their respective Certificate Programs.
Student online resources: https://www.library.miami.edu/
For accessing certain resources, students may need to go through UM IT authentication and enter their CaneID and passwords. Students can retrieve their CaneID and password at: https://caneidhelp.miami.edu/
Students are also able to check out books in person. To do so, they may stop by the Access Services desk to pick up a library card on their first visit to the Richter Library Building, located at Otto G. Richter Library Building, 1300 Memorial Drive, Coral Gables, Florida 33146