Machine Learning (CS 512)

2021 Fall
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
10.00
Öznur Taştan Okan otastan@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Recitation
Interactive,Communicative,Discussion based learning,Project based learning
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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

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.

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.

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.