We’ve all been to various .edu sites to the engineering courses only to find a Power Point Presentation with little information.
Stanford Engineering’s Machine Learning Course is an exception.
You have access to video of all the lectures, transcripts of all the lectures and class notes. I started working through it last week, brushing up on old topics and filling in gaps in my knowledge. Of all the online artificial intelligence and machine learning courses online, this is the best I’ve seen so far. The math is very low level, programming minimal, you’ll find it a very easy to work through introduction to machine learning.
Artificial Intelligence | Machine Learning
Instructor: Ng, AndrewThis course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background:Prerequisites: - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)