When Sebastian sent me the new nanodegree heads up I was mildly excited and now as you filled in the actual content I can't wait. With MIT's Underactuated Robotics at edX (which was fun) this is probably the most exciting set of courses I can see on the Internet these days!
Great questions. We limit the first batch for two reasons:
1. This is the first batch of the program ever and we want to make sure it's great before opening it up. We want to use this batch to learn and improve and then focus on larger class sizes.
2. One unique thing about Udacity is we provide real human services throughout the program such as a mentor, a code reviewer, and a career support rep. Many of these individuals are actually current or former students of our program. Since this program has never been done before, we are limited in how many such people we can find. Once we get more students in the program, that pool will naturally expand.
Best of luck and thanks for applying!
+ Particle filters?
+ SLAM?
+ Sensor fusion?
+ Bayesian methods?
+ VC dimension?
+ Sparsity?
+ Nonconvexity?
+ Deep learning?
I think it's gonna be a challenge to do useful machine learning for self-driving cars if you're not familiar with quite a range of math-heavy topics.
It's a difficult, 9 month program where we cover the following (some of which you mentioned):
- Computer Vision and OpenCV
- Deep Learning
- Sensor Fusion (Radar, Lidar)
- State Tracking with Filters (Kalman, Particle) and Localization
- Controllers
- Vehicle Dynamics
We have come up with this curriculum after talking to the heads of Engineering at Mercedes Benz, Otto, and NVidia at length. In addition, we have an open ended section where students can dive deeper into an area of their choosing.
Like you mentioned, a lot of this is Math heavy, which is why we have applications to enroll. Hope that answered your question!
I'm not sure if Udacity is even interested in teaching these types of courses, fwiw... though, i know Khan has lots of math courses.
We do have a few math courses such as our Intro To Statistics class (https://www.udacity.com/course/intro-to-statistics--st101) and our Linear Algebra class (https://www.udacity.com/course/linear-algebra-refresher-cour...) but I'd still recommend Khan Academy or MIT OCW's math offerings. The 18.06 class on OCW by Professor Strang is really awesome in particular.
I would be grateful if you provide good (math) fundamentals. It's much harder to understand how things work if you have to search for it yourself. Suppose you've never heard about a spark (https://en.wikipedia.org/wiki/Spark_(mathematics)), a Hilbert space (https://en.wikipedia.org/wiki/Hilbert_space), Metropolis-Hastings (https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_al...), or a Dirichlet Process (https://en.wikipedia.org/wiki/Dirichlet_process), then it's not so easy to find such concepts and understand their significance.
(1) If you need some help, you can find me here. I'm currently playing with MCMCs in nonparametric Bayesian methods that adapt to the structure in the real world. It is a waste to sample everything. For examples aisles in a supermarket have structure to them. In the "visual grammar" of the supermarket, they are aligned with each other. MCMC that can encapsulate this type of grammar will mix much faster.
(2) If you find a pupil interested in the combination of transfer learning and deep learning, feel free to refer to me. I'm not interested from the viewpoint of domain adaptation, but from the viewpoint of robotic communication.
- Hard real-time
- Fault tolerance
- Safety engineering
- Human factors analysis
The stuff that kills people. An automatic driving project should have someone from avionics design on board.
Also, what will the workload be like? Can this be done on evenings/weekends while holding a full time job?