Class Meets
When: Tuesdays and Fridays, 3:25pm-5:05pm
Where: Behrakis Health Sciences Center 310. See map.
Course Objectives
The course objective is to study the theory and practice of constructing algorithms that learn (functions) and make optimal decisions from data and experience. Machine learning is a field with goals overlapping with other disciplines, in particular, statistics, algorithms, engineering, or optimization theory. It also has wide applications to a number of scientific areas such as finance, life sciences, social sciences, or medicine.
Prerequisites
CS 5800 or CS 7800 with a minimum grade of C-.
Class Materials
Textbooks:
Pattern Recognition and Machine Learning — by C. M. Bishop, Springer, 2006.
Machine Learning: A Probabilistic Perspective — by K. P. Murphy, The MIT Press, 2012
Recommended books:
The Elements of Statistical Learning — by T. Hastie, et al., Springer, 2009.
Machine Learning — by T. M. Mitchell, McGraw-Hill, 1997
Supplementary materials: to be provided in class.
Topics
- Mathematical foundations of machine learning (random variables and probabilities, probability distributions, high-dimensional spaces, principles of optimization)
- Overview of machine learning (supervised, semi-supervised, unsupervised learning)
- Basics of parameter estimation (point estimates, Bayesian formulation, expectation-maximization algorithm)
- Classification algorithms: linear and non-linear algorithms (perceptron, logistic regression, naive Bayes, decision trees, neural networks, support vector machines)
- Regression algorithms (least squares linear regression, neural networks, relevance vector machines)
- Kernel methods (taught within classification and regression; dual representations, RBF networks)
- Ensemble methods (bagging, boosting, random forests)
- Practical aspects in machine learning (data preprocessing, overfitting, accuracy estimation, parameter and model selection)
- Special topics, if time permits (introduction to PAC learning, sample selection bias, learning from graph and time series data)
Grading
- Midterm exam: 20%
- Final exam: 20%
- Homework assignments: 30%
- Mini project: 25%
- Class participation: 5%
Late Policy and Academic Honesty
All assignments and exams are individual, except when collaboration is explicitly allowed. All the sources used for problem solution must be acknowledged, e.g. web sites, books, research papers, personal communication with people, etc. Academic honesty is taken seriously; for detailed information see Office of Student Conduct and Conflict Resolution.
Last updated: April 15, 2024