## 10-715 Fall 2020: Advanced Introduction to Machine Learning

This course is designed for Ph.D. students whose primary field of study is machine learning, or who intend to make machine learning methodological research a main focus of their thesis. It will give students a thorough grounding in the algorithms, mathematics, theories, and insights needed to do in-depth research and applications in machine learning. The topics of this course will in part parallel those covered in the general graduate machine learning course (10-701), but with a greater emphasis on depth in theory and algorithms. The course will also include additional recent topics such as fairness in machine learning.

**IMPORTANT NOTE:** Students entering the class are expected to have a pre-existing strong working knowledge of linear algebra (e.g., mathematical representation of subspaces, singular value decomposition), probability (e.g., multivariate Gaussians, Bayes' rule, conditional expectation), and calculus (e.g., derivative of a vector with respect to another vector), and programming (we'll mostly be supporting Python). This class is best for you if you have machine learning at the **CORE** of your studies/research, and want to understand the fundamentals. This class will be **HEAVY** and will move **FAST**. If machine learning is an auxiliary component of your studies/research or if you do not have the required background, you may consider the general graduate Machine Learning course (10-701) or the Masters-level Machine Learning course (10-601). Click here for an ML course comparison.

**Waitlist:** If you are in the waitlist and meet the aforementioned requisites, please send an email to the instructor and cc Diane Stidle (stidle@andrew.cmu.edu) outlining how you meet them (e.g., some courses you took on these topics etc.) Note that machine learning itself is NOT a prerequisite for this course. Also note that by departmental policy, this course is open only to graduate students (5th year masters are allowed). We expect that the waitlist will be cleared after the first week of classes, when everyone has had a chance to experience this class (and the other classes they are choosing between), and decide whether this class is a right fit for them or not.

**Instructor: **Nihar Shah

**Syllabus:** The topics covered will be similar to those covered last year.

**References:**

**[SB]** Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David (available online)