From the Arizona State University Master of Computer Science

AI and Machine Learning MasterTrack™ Certificate

Gain in-demand skills in artificial intelligence and machine learning by studying statistical machine learning, deep learning, supervised and unsupervised learning, knowledge representation and reasoning from the #1-ranked school for innovation in the U.S.

The next session starts on August 20, 2020. Enrollment is open from now through August 6, 2020.

Earn credit towards a Master’s Degree

This MasterTrack Certificate can qualify as credit towards your degree.

Institution

Earn Credit Towards a Degree If you get a B or better on your first attempt in every course in this MasterTrack™ Certificate, you will earn a university-issued certification, as well as satisfy the GPA requirement for the ASU Master of Computer Science degree program. Apply these credits to the Master of Computer Science at Arizona State University to begin the program with 9 of your 30 required credits completed. You must still meet all other admission criteria in order to be eligible for the degree program.

About this Online Certificate Program

Learn the theories and techniques used by practitioners in the field of artificial intelligence and machine learning.

What you will learn

  • The mathematics (Statistics, Probability, Calculus, Linear Algebra and optimization) needed for designing machine learning algorithms
  • Common learning paradigms and how to implement foundational algorithms in statistical machine learning
  • The principles, processes, and core concepts involved in designing autonomous agents
  • How to design, train, and optimize deep neural networks
  • The foundations of knowledge representation and reasoning, and how to identify which techniques are appropriate for which tasks

Skills you will gain

  • Supervised learning
  • Unsupervised learning
  • Deep learning architectures
  • Machine learning methods
  • Probabilistic inference
  • Image classification
  • Representation and reasoning
  • Globe

    100% online courses

  • Money

    $4,500

    Students pay by course. You will also be asked to pay an application fee when registering through the ASU website.

  • Clock

    4-6 months to complete

  • Intermediate Level

    Intermediate Level

    You should have an understanding of the following topics: Basics of algebra, linear algebra, probability, statistics, calculus, and algorithm design and analysis. Knowledge of programming in Python, ROS, and classical logic is also recommended.

  • Layer

    5 hands-on projects

  • Comment Dots

    Questions?

    Contact our enrollment team at ASU-AI-MT@coursera.org.

  • Institution

    Earn academic credit

    Apply these credits to the Master of Computer Science at Arizona State University to begin the program with 9 of your 30 required credits completed.

What is a MasterTrack™ Certificate?

With MasterTrack™ Certificates, portions of Master’s programs have been split into online modules, so you can earn a high quality university-issued career credential at a breakthrough price in a flexible, interactive format. Benefit from a deeply engaging learning experience with real-world projects and live, expert instruction. If you are accepted to the full Master's program, your MasterTrack coursework counts towards your degree.

Graduation Cap

Master’s Degree Learning

Take an online module of a Master's degree program that features live expert instruction and feedback combined with interactive team-based learning.

Layer

Boost Your Career

Receive a university-issued MasterTrack Certificate from a top university that you can add to your resume and LinkedIn profile.

MembersMembers

Build Your Portfolio

Demonstrate your skills through real-world projects and create work samples that help you stand out in your job search.

Stacked File

Earn Credit Towards a Degree

If you get a B or better on your first attempt in every course in this MasterTrack™ Certificate, you will earn a university-issued certification, and can apply these 9 credits to the Master of Computer Science at Arizona State University.

Interactive Learning Experience

You'll learn through readings, videos, graded assignments, real-world applied projects, and live global classroom sessions.

Live Global Webinars

Live Global Webinars

Feedback from Instructors and Experts

Feedback from Instructors and Experts

Real-World Projects

Real-World Projects

4 Courses in this MasterTrack™ Certificate

Take 3 of the 4 courses listed to earn your certificate.

Courses include:

  • Pre-recorded videos
  • Live sessions and office hours
  • Real-world projects
  • Peer collaboration
  • Web and mobile access

Statistical Machine Learning

Deriving generalizable models from training data is central to statistical machine learning. Statistical machine learning has found wide applications in many fields including artificial intelligence, computer vision, natural language processing, finance, bioinformatics, and more. This course provides a systematic introduction to common learning paradigms in statistical machine learning, accompanied by an exploration of a set of foundational algorithms.

Specific topics covered include:

  • Mathematical foundations for machine learning
  • Maximum likelihood estimation
  • Naive Bayes classification
  • Logistic regression
  • Support vector machines
  • Probabilistic graphical models
  • Mixture models
  • K-means clustering
  • Spectral clustering
  • Dimensionality reduction
  • Principal component analysis
  • Neural networks and deep learning
  • Convolutional neural networks
See all 4 Courses

Industry-relevant hands-on projects to build your portfolio

Density Estimation and Classification

Clock
3 hours

WHAT YOU WILL LEARN

  • How to extract features for both training set and testing set.
  • How to estimate/compute the parameters of the relevant distributions.
  • How to implement the Naïve Bayes Classifier and use it to produce a predicted label for each testing sample.
  • How to compute the classification accuracy.
  • How to write a short report summarizing the results, including the estimated parameters of the distributions, and the final classification accuracy.

Unsupervised Learning (K-means)

Clock
3 hours

WHAT YOU WILL LEARN

  • How to implement the k-means algorithm with Strategy 1.
  • How to use your code to do clustering on the given data; compute the objective function as a function of k (k = 2, 3, …, 10).
  • How to implement the k-means algorithm with Strategy 2.
  • How to use your code to do clustering on the given data; compute the objective function as a function of k (k = 2, 3, …, 10).
  • How to write a short report summarizing the results, including the plots for the objective function values under different settings.

Classification Using Neural Networks and Deep Learning

Clock
3 hours

WHAT YOU WILL LEARN

  • How to run the baseline code (as provided) and report the accuracy.
  • How to change the kernel size to 5*5, redo the experiment, plot the learning errors along with the epoch, and report the testing error and accuracy on the test set.
  • How to change the number of the feature maps in the first and second convolutional layers, redo the experiment, plot the learning errors along with the epoch, and report the testing error and accuracy on the test set.
  • How to write a brief report summarizing the results.
See all 8 projects

What industry partners are saying

Linda Zaruches - Industry Recruiter and Alumni, GoDaddy
“GoDaddy has been recruiting ASU Computer Science students for the past 8 years. We love ASU students because they are passionate, smart, scrappy and dedicated to making a difference. Watching our ASU hires excel in their careers and make significant contributions to our success and our customers’ success is very rewarding. We look forward to continuing our long-standing relationship with ASU in hopes of hiring some of the best and brightest developers.”...
Megan Shaffer - Talent Acquisition Manager, Allstate
“Allstate keeps coming back to ASU Computer Science for the diverse talent pipeline. ASU offers many opportunities for us to engage with students, so we can hire efficiently and better serve our customers. We look forward to partnering for future career fairs, hackathons and student organization events.” ...

Instructors

Baoxin Li, Ph.D.

Baoxin Li, Ph.D.

Professor

Yu Zhang, Ph. D.

Yu Zhang, Ph. D.

Assistant Professor

Heni Ben Amor, Ph. D.

Heni Ben Amor, Ph. D.

Assistant Professor

Yezhou Yang, Ph. D.

Yezhou Yang, Ph. D.

Assistant Professor

Siddharth Srivastava, Ph. D.

Siddharth Srivastava, Ph. D.

Assistant Professor

Joohyung Lee, Ph. D.

Joohyung Lee, Ph. D.

Associate Professor

Hemanth Venkateswara, Ph. D.

Hemanth Venkateswara, Ph. D.

Assistant Research Professor

Frequently Asked Questions

More questions? Visit the Learner Help Center.

Coursera does not grant academic credit; the decision to grant, accept, or recognize academic credit, and the process for awarding such credit, is at the sole discretion of the academic institutions offering the MasterTrack™ Certificate program and/or other institutions that have determined that completion of the program may be worthy of academic credit. Completion of a MasterTrack™ Certificate program does not guarantee admission into the full Master’s program referenced herein, or any other degree program.