Blog School of Autonomous Systems Machine Learning at Udacity Goes Deeper

Machine Learning at Udacity Goes Deeper

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Machine Learning - Udacity

The Machine Learning Engineer Nanodegree program has been one of Udacity’s benchmark programs for over 2 years. Thousands of students have graduated the program, and many have gone on to great careers at companies like Google, Amazon, and more. As technology evolves, so does our curriculum, and we think much of the program’s success can be attributed to keeping the content up-to-the-minute current.

In this post, I’m going to share some exciting new updates to our Machine Learning Engineer Nanodegree program, including enhanced sections on Reinforcement Learning, Supervised Learning, and Unsupervised Learning, plus more conceptual content, new labs, and a cutting-edge quadcopter project!

(Deep) Reinforcement Learning

Important recent advances in AI share a common factor: Deep Reinforcement Learning. Whether we’re talking about DeepMind successfully applying deep reinforcement learning to beat the world’s best human Go player, or Google using deep reinforcement learning for resource optimization, the story is the same—Deep Reinforcement Learning is having a profound impact on the advance of artificial intelligence. To ensure you’re getting the opportunity to master these valuable skills, we have rebuilt the existing traditional reinforcement learning section, and added a new section on deep reinforcement learning, which covers the most cutting-edge advancements in this discipline, such as temporal-difference methods, deep Q-learning, policy gradients, and actor-critic methods.

Deep reinforcement learning is having a particularly profound impact on the fields of robotics and autonomous flight, and we cover both with an amazing new project in which you’ll design a deep reinforcement learning agent to control several quadcopter flying tasks, including take-off, hover, and landing!

Enhanced Conceptual Content

In our program, we strive to balance hands-on projects with enhanced conceptual content, and this is one of the key differentiators of our approach. We want to reach beyond code and formulas, to teach machine learning concepts. In our classroom, you don’t just master a technology, you develop an intuition for it—you’re going to learn to think machine learning!

To ensure you gain a thorough understanding of the most important topics in machine learning, we have rebuilt the supervised and unsupervised content from scratch, including the sections on Linear Regression, Naive Bayes, Decision Trees, Support Vector Machines, Ensemble Methods, as well as Clustering, Gaussian Mixture Models, and Dimensionality Reduction.

Groundbreaking Curriculum

In addition to the enhanced conceptual content detailed above, we have also added a new section taught by Sebastian Thrun. Sebastian’s influence in the field of autonomous transportation has helped inform the development of our Self-Driving Car programs, and as he now turns his attention to applications of deep learning in healthcare, we’re excited to introduce his work to you through our Machine Learning Engineer Nanodegree program.

In this new section, Sebastian specifically discusses his groundbreaking work using deep learning to detect skin cancer with greater precision than dermatologists. His efforts, and the work of his team at Stanford, have recently been showcased in Nature magazine, and The New Yorker.

“If a dermatologist can do it, then a machine should be able to do it as well. Perhaps a machine could do it even better.” —Sebastian Thrun

This spirit of possibility and potential is central to our approach, and we’re excited for you to experience all of the latest additions to our curriculum!

Term Structure

Simultaneous to rolling out our new content offerings, we are also reorganizing the program into a term-based model. This structural enhancement enables us to far better support a stable and engaged student community that moves through the curriculum at a consistent and steady pace. This makes possible more productive collaboration opportunities for students, and enables our mentors to engage more deeply with the challenges you face and the projects you work through.

Tuition for our term-based program is simple—a one-time upfront price of $999 for each term. The program is made up of two terms: Machine Learning Foundations, and Advanced Machine Learning. Each term is three months long. To successfully complete the program, you’ll to need to complete both terms. We estimate you’ll need to spend 10 hours/week on your coursework to complete your studies on time.

The Power of Machine Learning

The field of machine learning is changing every day, and improving the lives of all humans as it advances. As Sebastian Thrun says:

“Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.”

As a graduate of this Nanodegree program, you will become a part of this forward-looking revolution. You will bring your skills to bear on some of the most compelling and important challenges of our time. Enroll in the Machine Learning Engineer Nanodegree Program today, and experience the power of machine learning as you embark on the journey of a lifetime!

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