Machine learning is basically a shift from the classical idea of “let’s have computers act like a human” to “let’s have computers perform statistical tasks which they’re good at.” This course covers several major ideas in this field.
Programming skills, linear algebra, probability. There’s a prerequisite quiz early in the course.
- Concept Learning
- Instance-based Learning
- Decision Trees
- ML Experimentation
- Linear Rules
- Support Vector Machines
- Generative Models
- Hidden Markov Models
- Structured Output Prediction [Super interesting area of research in Machine Learning right now]
- Learning Theory [VC Dimension]
5 assignments, 5 quizzes, 2 prelims, and a final project.
Students have a week to complete assignments and can be done in pairs. There are typically 1-2 weeks between the due date of an assignment and the next being released. Assignments are a mix of programming and theoretical problems. Getting a reliable partner is important as doing assignments can take a long time.
Quizzes are in class and are straightforward and short.
Prelims are in-class.
Final project is on a subject of your choice, but must use machine learning.
A course that’s worth taking. ML is becoming more important in every field of CS nowadays, and it helps to have a working knowledge of it so you don’t have to blank out when people bring it up.
Easily one of the best classes I’ve taken at Cornell. Perfect mix of theory and application. Also, Thorsten is amazing at teaching. Tests are also pretty straightforward, and projects are not complicated.
You can watch recorded lecture from Fall 2011 (they are listed under Fall 2012) on the Cornell Videonote website at 
|Semester||Time||Professor||Median Grade||Course Page|
|Fall 2013||-||Thorsten Joachims||B||http://www.cs.cornell.edu/courses/cs4780/2011fa/|
|Fall 2011||-||Thorsten Joachims||B||http://www.cs.cornell.edu/courses/cs4780/2011fa/|