Machine Learning (CS 412)

2021 Fall
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
10.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Ayşe Berrin Yanıkoğlu berrin@sabanciuniv.edu,
English
MATH203 MATH201
Formal lecture,Recitation
Interactive,Learner centered,Project based learning

CONTENT

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.

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.

ASSESSMENT METHODS and CRITERIA

 Percentage (%) Final 35 Exam 30 Participation 5 Group Project 10 Homework 20