SWE 582 Machine Learning for Data Analytics, Fall 2015
Instructor:
A. Taylan Cemgil
For contact details and further information see the web: http://www.cmpe.boun.edu.tr/~cemgil/
Notebooks
Some class notes can be found as iPython notebooks on various topics:
Course Description
Data mining deals with extracting patterns from large and heterogeneous data sets by combining methods from statistics and artificial intelligence with database management.
Data mining provides computational tools to transform raw data into useful information. As such, it is becoming an increasingly important branch of computer science useful for a wide range of applications, such as marketing, surveillance, fraud detection, credibility assessment, recommendation systems and scientific discovery.
This course is designed as a continuation of a basic introduction course as SWE546 Data Mining. The aim here is to provide a detailed review of modern machine learning techniques and the underlying mathematical theory. As such, it is suitable for students with some previous exposure to Data mining who want to elaborate their knowledge on machine learning.
Prerequisite
SWE546 or equivalent, or consent of the instructor
Topics
Introduction, Summary of Data Mining
Probability Theory Review, Graphical Models
Construction of Probabilistic models, Hierarchical Modeling,
Bayesian Linear Models
Gaussian Processes
Sequential Data
Hidden Markov Models (HMM's),
Data Visualization
Data Fusion, Tensor factorization models
Nonlinear Optimization Techniques
Scaling up Machine Learning
Reference Books
Slides
../ipynb/Sampling.html
Handouts
Linear Algebra Review
Matrix Calculus
Probability Theory
A short cribsheet by Ian Murray
Computer Usage
Homeworks require using Python or Octave
Grading
Class Participation, Classwork and Homeworks 0.50
1 Final Project, to be presented in class 0.30
1 Written Exam 0.20
