Well, I'm an engineer by day. Although most of my work revolves around modeling, we generally do pretty basic stuff. An "Advanced" model would be a monte carlo simulation validated using R2 tests.
Currently, in my field, there is a lot of research using Logistic and bayesian analysis.
My question is, which courses would you recommend someone to take from MIT's open course site or any other sites, for someone who learns best by video/audio first, and reading second?
What i'd like to learn are the following:
- Be able to understand the models and when to employ them
- able to take in field data (which is generated once and cannot be regenerated) and design and perform experiments
- Able to understand the results, look at them, and figure out if something is off, "show stopper" or "outliers", or if everything is fine and dandy
- Be able to validate and calibrate the model, to actual "As-built" results
- Be able to forecast the results using appropriate sensitivity analysis
- be able to forecast / "plug" missing data
- be able to write journal papers related to my field
my field in a nutshell is: transportation demand modeling for passenger vehicles, using either the generic four step model, or socio economic activity/tour based models such as PECAS or urbansim
I would go straight to VideoLectures.net. This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on statistics, forecasting, and machine learning. The target audience for these video lectures ranges from beginner (some lectures are specifically tagged as "tutorials") to expert; most of them seem to be somewhere in the middle.
All of the lectures and tutorials are taught to highly experienced professionals and academics, and in many instances, the lecturer is the leading authority on the topic he/she is lecturing on. The site is also 100% free.
The one disadvantage is that you cannot download the lectures and store them in e.g., itunes; however, nearly every lectures has a set of slides which you can download (or, conveniently, you can view them online as you watch the presentation).
YouTube might have more, but even if you search Y/T through a specific channel, i am sure the signal-to-noise ratio is far higher--on VideoLectures.net, every lecture i've viewed has been outstanding and if you scan the viewer reviews, you'll find that's the consensus opinion towards the entire collection.
A few that i've watched and that i can recommend highly:
Basics of Probability and Statistics
Introduction to Machine Learning
Gaussian Process Basics
k-Nearest Neighbor Models
I've only had a little look at this lecture series on Machine Learning, but it looks good.
Lecture 11 covers Bayesian Statistics and Regularization.
try the machine learning summer school La Palma 2012
Coursera is offering a wide a range of online lectures. The Machine Learning lecture by Andrew Ng covers logistic regression and regularization in the beginning. Furthermore, Probabilistic Graphical Models by Daphne Koller might be of interest to you as well.