Machine Learning (CS 512)

2022 Fall
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
10
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
Business Analytics - With Thesis *
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Data Science - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Management Ph.D. - With Bachelor's Degree *
Management Ph.D. - With Master's Degree *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mathematics - With Bachelor's Degree *
Mathematics - With Master's Degree *
Mathematics - With Thesis *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree *
Physics - With Master's Degree *
Physics - With Thesis *

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 OUTCOMES

  • 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.