Statistical Machine Learning

 with  Larry Wasserman

Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming students have taken Machine Learning (10-715) and Intermediate Statistics (36-705). The term “statistical” in the title reflects the emphasis on statistical theory and methodology. The course combines methodology with theoretical foundations. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research. The course includes topics in statistical theory that are important for researchers in machine learning, including nonparametric theory, consistency, minimax estimation, and concentration of measure.

1. Review: probability, bias/variance, mle, regression, classification. 2. Theoretical Foundations (a) Function Spaces: Holder spaces, Sobolev spaces, reproducing kernel Hilbert spaces (RKHS) (b) Concentration of Measure (c) Minimax Theory 3. Supervised Learning (a) Linear Regression: low dimensional, ridge regression, lasso, greedy regression (b) Nonpar Regression: kernel regression, local polynomials, additive, RKHS regression (c) Linear Classification: linear, logistic, SVM, sparse logistic (d) Nonpar Classification: NN, naive Bayes, plug-in, kernelized SVM (e) Conformal Prediction (f) Cross Validation 4. Unsupervised Learning (a) Nonpar Density Estimation (b) Clustering: k-means, mixtures, single-linkage, density clustering, spectral clustering (c) Measures of Dependence (d) Graphical Models: correlation graphs, partial correlation graphs, cond. indep. graphs 5. Other Topics (a) Nonparametric Bayesian Inference (b) Bootstrap and subsampling (c) Interactive Data Analysis (d) Robustness (e) Active Learning (f) Differential Privacy (g) Deep Learning (h) Distributed Learning (i) Streaming

0 Student
+ Add to My Courses
Learn Data Analysis

Learn to become a Data Analyst. Job offer guaranteed or get a full refund.

Become a Data Scientist

Learn Python & R at your own pace. Start now for free!

FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

0 reviews for Statistical Machine Learning

Write a review

Write a review

How would you rate this course? *
How much of the course did you finish? *
Create Review