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Introduction to Statistical Learning and Inference with Application in R

The course is aim to introduce the basic ideas of statistical learning and predictive modeling from a statistical, theoretical and computational perspective, together with applications in real data. Topics cover the major schools of thought that influence modern scientific practice. We aim to provide a very applied overview to some classical linear approaches such as Linear Regression, Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors Algorithm, K-means Clustering, as well as some modern non-linear methods as Generalized Additive Models, Decision Trees, Boosting, Bagging and Support Vector Machines.

 

Intermediate Excel for Personal and Professional Use

This course targets new or recent Excel users as well as existing users who are keen on developing more advanced skills for personal and/or professional use. This course is intended for anyone interested in learning to use Excel dynamically and efficiently to store, organize, summarize, analyze, and visualize data.



Scalable Machine Learning: Methods and Tools

This course introduces students to common methods and tools for machine learning in practice and how to run at scale for large-scale data. A number of common methods in data transformation, unsupervised learning, supervised learning and deep learning techniques will be introduced including principle component analysis, multi-dimensional scaling, K means clustering, Gaussian Mixture Model, Regression, supported vector machine, Naïve Bayesian classification, decision tree and random forest. Several deep neural network structure, including autoencoder, convolutional neural network, and recurrent neuron network will also be introduced.



Statistical Methods for Categorical Data – Logistic Regression and Beyond

The course will cover traditional general-purpose models for categorical data, including logistic and probit binomial and multinomial regression models, Markov chains etc. which will then provide a background for more advanced methods suited to address modern era complex and high dimensional challenges.

 

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