Course Syllabus

Syllabus

Machine Learning

CSCI-B555 - Fall 2016

 

Class Meets

When: Monday and Wednesday 4:00 pm -5:15pm

Where: Jordan Hall, JHA 100

 

Instructor

Martha White

Office: LH 401E

Email:.martha@indiana.edu.

Web: http://homes.soic.indiana.edu/martha

 

AI

Inhak Hwang

Office: LH 406

Email:.inhahwan@indiana.edu.

 

Raksha Kumaraswamy

Office: LH 406

Email:.rakkumar@indiana.edu

 

Erfan Sadeqi Azer

Office: LH 406

Email:.esadeqia@indiana.edu

 

Andrew Patterson

Office: LH 406

Email:.andnpatt@indiana.edu.

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Office Hours (instructor)

Tuesday 3pm-5pm, or by appointment in LH 401E

 

Office Hours (TAs)

Tuesday 2:30pm-4:00pm with Inhak in LH 215D 

Wednesday 11:00pm-12:30pm with Erfan and Raksha in LH 325 

 

Course Objective

The course objective is to study the theory and practice of constructing algorithms that learn (functions) from data. 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

Graduate student standing or permission of the instructor.

 

Textbooks

Main notes will be provided in class.

 

 

Recommended

Pattern Recognition and Machine Learning - by C. M. Bishop, Springer 2006.

 

Additional

Machine Learning - by Tom M. Mitchell, McGraw-Hill, 1997

The Elements of Statistical Learning - by T. Hastie, R. Tibshirani, and J. Friedman, 2009

 

Grading

Thought questions: 10%

Final exam: 35%

Homework assignments: 40%

Quizzes: 15%

 

Topics: about 75% of the following topics depending on the year

▪  mathematical foundations of machine learning

▫  random variables and probabilities

▫  probability distributions

▫  high-dimensional spaces

▪  overview of machine learning

▫  supervised, semi-supervised, unsupervised learning

▫  inductive and transductive frameworks

▪  basics of parameter estimation

▫  maximum likelihood and maximum a posteriori

▫  Bayesian formulation

▪  classification algorithms: linear and non-linear algorithms

▫  perceptrons

▫  logistic regression

▫  naive Bayes

▫  decision trees

▫  neural networks

▫  support vector machines

▪  regression algorithms

▫  least squares linear regression

▫  neural networks

 

▪  kernel methods (taught within classification and regression)

▪  representation learning and matrix factorization

▫ (nonlinear) dimensionality reduction

▫  sparse coding

▪  basics of graphical models

▫  Bayesian networks, e.g., hidden Markov model

▫  inference and estimation

▪  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 data

▫  learning from sequential data

 

 

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 Indiana University Code of Student Rights, Responsibilities, and Conduct.