COMP 790125 (Spring 2017) — Advanced Machine Learning
Modern techniques in machine learning
Organizational
Time: Tuesday, Thursday 12:301:45
Place:
FB 007
Prerequisites: Linear algebra, Probability or Statistics, programming
(Matlab/R/Python)
Instructor: Vladimir Jojic (vjojic@cs.unc.edu)
Office hours: SN 319 Tuesdays
and Thursdays 2pm3pm
Overview
Rapid accumulation of diverse types of data necessitates innovation in data analysis. Machine learning is a growing field that has found numerous applications ranging from computer vision, speech, and medicine.
Structure
The course aims to engage you in solving problems using machine learning. In order to achieve this, the course will consist of three components
 Lectures covering ML and applications
 Homework assignments
 Student project or a written survey
Grading

3 credits:
 Homework assignments (4): 40%
 Project and project paper: 60% Example project proposal [Zip]
Topics covered
Machine Learning
 Generative and discriminative models
 Fully Bayesian approaches: Gaussian Processes
 Graphical models: inference and learning
 Approximate inference: Variational and MCMC
 Deep learning
Textbook
There is no textbook for this course, but you may find following helpful: "Machine Learning: A Probabilistic Perspective." Kevin P. Murphy
 "Pattern Recognition and Machine Learning," Chris M. Bishop
 "Probabilistic Graphical Models," Daphne Koller and Nir Friedman
 "The Elements of Statistical Learning: Data Mining, Inference, and Prediction," T. Hastie, R. Tibshirani, J. Friedman, download
 "Information Theory, Inference, and Learning Algorithms," David MacKay, download
 "Deep Learning," Y. Bengio, I. J. Goodfellow, A. Courville download
Links
 Linear Algebra refresher/refrence
 Probability refresher/refrence
 Matrix Reference Manual
 LaTeX short
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