This is an introductory machine learning course that will aim a solid understanding of the fundamental issues in machine learning (overfitting, bias/variance), together with several state-of-art approaches such as decision trees, linear regression, k-nearest neighbor, Bayesian classifiers, neural networks, logistic regression, and classifier combination.
Machine Learning (CS 412)
Programs\Type | Required | Core Elective | Area Elective |
BA- Political Science | |||
BA-Cultural Studies | |||
BA-Cultural Studies | |||
BA-Economics | |||
BA-Economics | |||
BA-International Studies | |||
BA-International Studies | |||
BA-Management | |||
BA-Management | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Social & Political Sciences | |||
BA-Visual Arts&Visual Com.Des. | |||
BA-Visual Arts&Visual Com.Des. | |||
BS-Biological Sci.&Bioeng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Electronics Engineering | * | ||
BS-Electronics Engineering | * | ||
BS-Industrial Engineering | * | ||
BS-Manufacturing Systems Eng. | * | ||
BS-Materials Sci. & Nano Eng. | * | ||
BS-Materials Science & Eng. | * | ||
BS-Mechatronics | * | ||
BS-Mechatronics | * | ||
BS-Microelectronics | |||
BS-Molecular Bio.Gen.&Bioeng | * | ||
BS-Telecommunications | * | ||
Business Analytics | |||
Decision and Behavior | |||
Physics |
CONTENT
OBJECTIVE
To teach fundamentals of Machine Learning for students of all backgrounds, so that they will know its capabilities, limitations and be able to design all aspects of a learning system.
Learning Objectives:
1. Understand the basic concepts, issues, assumptions and limitations in machine learning (e.g. base accuracy, overfitting, bias/variance, curse of dimensionality...).
2. Have a working knowledge of the basic mathematics (probability, expectation, entropy, basic linear algebra, ...) and algorithms behind common machine learning techniques; together with their suitability in given situations.
3. Given a machine learning problem, be able to implement and evaluate one of the standard machine learning algorithms (e.g. decision trees, neural networks, SVMs) using a tool such as Weka/Matlab or a programming language Python/R.
LEARNING OUTCOME
Have a solid understanding of the basic concepts, issues, assumptions and limitations in machine learning and how they apply to various machine learning techniques.
Have a working knowledge of the basic mathematics (probability, expectation, entropy, basic linear algebra, ...) and algorithms behind common machine learning techniques; together with their suitability in given situations.
Given a machine learning problem, select, implement and evaluate one of the appropriate machine learning algorithms (e.g. decision trees, neural networks, SVMs) using a programming environment such as Weka or Matlab.
Update Date:
ASSESSMENT METHODS and CRITERIA
Percentage (%) | |
Final | 35 |
Exam | 30 |
Participation | 5 |
Group Project | 10 |
Homework | 20 |
RECOMENDED or REQUIRED READINGS
Textbook |
Intro. to Machine Learning - Ethem Alpaydın |
Optional Readings |
Advanced or alternative explanations will be provided as supplement to lecture slides and course textbook. |