## B555: Machine Learning

### Class Description

This course objective is to study the theory and practice of constructing algorithms that learn (functions) and choose 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. The class will cover theoretical foundations of machine learning but also provide examples from classification, regression, and statistic distribution learning. This is a core Computer Science course

### Class Information

**Semester(s): **Spring

**Semester(s) Offered: ** Spring

Download Syllabus

**Class time: **
Tuesday, Thursday : 9:30am-10:45am

**Website: **http://homes.soic.indiana.edu/martha/teaching.html

**Capacity: ** 50

### Contact Information

**Instructor: **Christopher Raphael

**Other Contact(s): ** Martha White - martha@indiana.edu

### Other Details

**Prerequisites: ** None.

**Algebra Required?: ** Basics: vector spaces, matrices, independence, solving linear systems.

**Calculus Required?: ** Basics: discrete and continuous functions, differentiation, integration.

**Day(s) per week offered: ** Two

**Books used: ** Recommended Textbook:
Pattern Recognition and Machine Learning - by C. M. Bishop, Springer 2006.
Additional materials:
Machine Learning - by Tom M. Mitchell, McGraw-Hill, 1997.
The Elements of Statistical Learning - by T. Hastie, R. Tibshirani, and J. Friedman, 2009.

**Applied/Theoretical: ** Balanced.

**Formal Computing Lab?: ** No

**Software Used: **
MatLab, Python

**How the software is used: ** To provide illustrative demos.

**Problem Sets: ** Four homework assignments and five thought questions.

**Data Analysis: ** Basic implementation and analysis of methods taught in class.

**Presentations: ** Traditional white-board; power point when needed; demos.

**Exams: ** Final exam (final week)

**Keywords: ** Machine Learning, statistical inference, classification, regression, distribution learning

**Comments: ** Instructor's code and demos are in MATLAB. Homework assignments will contain programming, but students are not required to use MATLAB.