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,
English
Doctoral, Master
--
Formal lecture,Recitation
Interactive,Communicative,Discussion based learning,Project based learning
Click here to view.

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 35
Exam 15
Group Project 20
Homework 30

RECOMENDED or REQUIRED READINGS

Textbook

Machine Learning by Ethem Alpaydin.

Optional Readings

Supplementary material (others' slides or related article) for graduate or interested students.

Course Web http://people.sabanciuniv.edu/cs512/