This is an introductory machine learning course that will aim a solid understanding of the fundamental issues in machine learning together with several ML techniques such as decision trees, k-nearest neighbor, Bayesian classifiers, neural networks, linear and logistic regression, dimensionality reduction, clustering, SVM and ensemble techniques. Some more theoretical aspects such as inductive bias and VC dimension will also be covered.
Machine Learning (CS 512)
Programs\Type | Required | Core Elective | Area Elective |
MA-European Studies | |||
MA-European Studies-Non Thesis | |||
MA-Political Science | |||
MA-Political Science-Non Thes | |||
MA-Visual Arts&Vis. Com Des-NT | |||
MA-Visual Arts&Visual Com Des | |||
MS-Bio. Sci. & Bioeng. LFI | |||
MS-Bio. Sci. & Bioeng. LFI-ENG | |||
MS-Biological Sci&Bioeng. | * | ||
MS-Business Analytics | |||
MS-Computer Sci.&Eng. LFI | |||
MS-Computer Sci.&Eng. LFI-ENG | |||
MS-Computer Science and Eng. | * | ||
MS-Cyber Security(with thesis) | * | ||
MS-Data Science | * | ||
MS-Elec. Eng&Comp Sc.LFI-ENG | |||
MS-Electronics Eng&Comp Sc.LFI | |||
MS-Electronics Eng&Computer Sc | * | ||
MS-Electronics Eng. | * | ||
MS-Electronics Eng. LFI | |||
MS-Electronics Eng. LFI-ENG | |||
MS-Energy Techno.&Man. | * | ||
MS-Industrial Eng. LFI-ENG | |||
MS-Industrial Engineering | * | ||
MS-Industrial Engineering LFI | |||
MS-Manufacturing Eng-Non Thes | * | ||
MS-Manufacturing Engineering | * | ||
MS-Materials Sci & Engineering | * | ||
MS-Materials Sci. & Eng. LFI | |||
MS-Materials Sci.&Eng. LFI-ENG | |||
MS-Mathematics | |||
MS-Mechatronics | * | ||
MS-Mechatronics LFI | |||
MS-Mechatronics LFI-ENG | |||
MS-Physics | |||
MS-Physics-Non Thesis | * | ||
MS-Psychology | |||
MS-Psychology-Non Thesis | |||
PHD-Biological Sci&Bioeng. | * | ||
PHD-Comp. Sci and Eng.after UG | * | ||
PHD-Computer Science and Eng. | * | ||
PHD-Cyber Security | * | ||
PHD-Electronics Eng&ComputerSc | * | ||
PHD-Electronics Eng. | * | ||
PHD-Electronics Eng. after UG | * | ||
PHD-Experimental Psychology | |||
PHD-Industrial Engineering | * | ||
PHD-Management | |||
PHD-Manufacturing Eng after UG | * | ||
PHD-Manufacturing Engineering | * | ||
PHD-Materials Sci.&Engineering | * | ||
PHD-Mathematics | |||
PHD-Mechatronics | * | ||
PHD-Mechatronics after UG | * | ||
PHD-Physics | |||
PHD-Physics after UG | |||
PHD-Social Psychology | |||
PHDBIO after UG | * | ||
PHDCYSEC after UG | * | ||
PHDEECS after UG | * | ||
PHDEPSY after UG | |||
PHDIE after UG | * | ||
PHDMAN after UG | |||
PHDMAN after UG-Finance | |||
PHDMAN after UG-Man. and Org. | |||
PHDMAN after UG-Op.&Sup. Cha. | |||
PHDMAN-Finance Area | |||
PHDMAN-Man. and Org. Area | |||
PHDMAN-Op. & Supp. Chain Area | |||
PHDMAT after UG | * | ||
PHDMATH after UG | |||
PHDSPSY after UG |
CONTENT
OBJECTIVE
To teach fundamentals of machine learning so that each student can select, implement and evaluate an appropriate machine learning technique for a given problem.
LEARNING OUTCOME
Understand the basic concepts, issues, assumptions and limitations in machine learning (e.g. overfitting, error measures, inductive bias...).
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, be able to implement and evaluate one of the standard 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 (%) | |
Midterm | 40 |
Group Project | 20 |
Homework | 40 |
RECOMENDED or REQUIRED READINGS
Textbook |
Machine Learning by Ethem Alpaydin. |
Optional Readings |
Supplementary material (others' slides or related article) for graduate or interested students. |