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BLG 454E - Learning From Data

Course Objectives

Introduce students to major data analytics and machine learning methods and
underlying theories
- Learning to apply available tools to solve classification, clustering and regression
- Learning to avoid major pitfalls such as overfitting, confusing correlation and causality
while using machine learning tools
- Learning the assessment and comparison of performance of machine learning methods

Course Description

Introduction to Machine Learning, major applications
Mathematical background, marginal and conditional Probability, Bayes theorem, Bayesian
decision theory
Density estimation, Maximum Likelihood estimate, Bayesian Learning, Naïve Bayes
Linear regression
Bias-variance dilemma, regularization, ridge regression and lasso
Linear classifiers
Artificial neural networks, perceptron and multilayer perceptron
Assessment and comparison of classifier performance
Feature selection and extraction
Large margin classifiers, support vector machines, kernel methods
Decision trees and random forest
Unsupervised learning, clustering
Deep learning and big data

Course Coordinator
Yusuf Yaslan
Course Language
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