Course Leader: Abdullahi Abdu IBRAHIM
Home Institution: Altinbas University, Turkey
Course pre-requisite(s): Calculus and Statistics
Machine learning as a field is now incredibly pervasive, with applications spanning from energy management in intelligent buildings to autonomous navigation, and from analyzing medical data to book recommendation systems, etc. This course will cover casting such problems in a supervised (generative/discriminative learning, parametric/non-parametric learning, neural networks) or unsupervised (clustering, dimensionality reduction, kernel methods) learning frameworks. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms without going deeply into the mathematics and gain practical experience applying them.
A student who successfully fulfills the course requirements will have demonstrated ability to gain knowledge about basic concepts of Machine Learning; Identify machine learning techniques suitable for a given problem; Solve the problems using various machine learning techniques
Introduction
Linear Regression with One Variable
Linear Regression with Multiple Variables
Logistic Regression
Logistic Regression
Languages & Frameworks
Midterm
Performance evaluation & improvement
Regularization
Deep learning and Neural Networks
The instructional method will be lectures
Stanford Professor Andrew Ng Machine Learning Material
Midterm Exam 50%
Final Exam 50%