A repo consisting of the coursework I did as a student at UofT. Each course has a corresponding folder, and the correspondance should be clear by the naming. In each of these folders are another set of folders, each containing the seperate projects done for the course. Each of these projects are accompanied by a README file. The README file contains a summary of the project, what I learned, the software used, and how to use the code.
Official Course Description
An introduction to software design and development concepts, methods, and tools using a statically-typed object-oriented programming language such as Java. Topics from: version control, unit testing, refactoring, object-oriented design and development, design patterns, advanced IDE usage, regular expressions, and reflection. Representation of floating-point numbers and introduction to numerical computation.
taken from here
Official Course Description
An introduction to probability as a means of representing and reasoning with uncertain knowledge. Qualitative and quantitative specification of probability distributions using probabilistic graphical models. Algorithms for inference and probabilistic reasoning with graphical models. Statistical approaches and algorithms for learning probability models from empirical data. Applications of these models in artificial intelligence and machine learning.
taken from here
Official Course Description
Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models, introduction to generalized linear models, graphical methods, additional topics such as random effects models, designed experiments, model selection, analysis of censored data, introduced as needed in the context of case studies.
taken from here
Course Description
The course will focus the using and interpreting advanced statistical methods with applications in a number of different areas. This course is designed for Master and PhD students in Statistics, and is REQUIRED for the Applied paper of the PhD Comprehensive Exams in Statistics. The course is a mixture of theory and applications, and will include a number of projects which will involve computing with R.
taken from here
Official Course Description
Statistical aspects of supervised learning: regression with spline bases, regularization methods, parametric and nonparametric classification methods, nearest neighbours, cross-validation and model selection, generalized additive models, trees, model averaging, clustering and nearest neighbour methods for unsupervised learning.
taken from here