CMP472-Introduction to Machine Learning

Fall 2026

Course Information:

Instructor: Prof. Dr. Hacer Yalım Keleş

Email: hacerkeles@cs.hacettepe.edu.tr

Lectures: Thursday, 09:40-12:30

Room: D10

Communication: Course Piazza Page

Office Hours: Thursday, 13:30-15:30

Aim:

This is an introductory lecture that provides the theoretical foundations of the various machine learning models and learning algorithms together with applications on different real/synthetic data.

Reference Textbooks:

Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer (2006) (PRML: available online)
Bayesian Reasoning and Machine Learning, David Barber. (BRML: available online)
Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press 2016 (available online)
Pattern Classification, R. O. Duda, P. E. Hart, D. G. Stork, (Second Edn.)

Tentative Course Schedule:

Week#	Date	Subject	Quiz timeline	Application timeline
1	24-Sep-2026	Introduction		https://www.kaggle.com/learn/python (optional)
2	1-Oct-2026	ML Basics with Linear Regression		(C1) https://www.kaggle.com/learn/pandas (get certified ->1%)*
3	8-Oct-2026	Basics of Linear Classifiers		(C2) https://www.kaggle.com/learn/data-cleaning/course (get certified-> 1% )*
4	15-Oct-2026	Perceptron Learning, Fisher’s LD
5	22-Oct-2026	Decision Trees and Ensemble Methods	Q1	(C3) https://www.kaggle.com/learn/intro-to-machine-learning (get certified -> 2%)
6	29-Oct-2026	(NO CLASS) Support Vector Machines Video Lecture (optional) available online MIT 6.034 AI Course	National Holiday	(C4) https://www.kaggle.com/learn/intermediate machine-learning (get certified->3% )*
7	5-Nov-2026	Logistic Regression, Multi-class Classification
8	12-Nov-2026	Midterm Exam I
9	19-Nov-2026	Feed Forward Neural Networks, Backpropagation Algorithm		(C5) https://www.kaggle.com/learn/intro-to-deep-learning (get certified -> 3%)*
10	26-Nov-2026	Convolutional Neural Networks, CNN Architectures		PA1
11	3-Dec-2026	Recurrent Neural Networks
12	10-Dec-2026	Attention and Transformer Networks	Q2	PA2
13	17-Dec-2026	MAP and MER Classifier, Naive Bayes Classifier
14	24-Dec-2026	Unsupervised Learning: PCA, Clustering, Autoencoders

*The Kaggle course uses Keras; PyTorch will be used for deep learning implementations in PAs.

Assessment:

Item	Weight
Quizzes	10%
Certifications	10%
PAs	20%
Midterm Exam I	20%
Final Exam	40%

Logistics:

All communication and announcements will be posted in the Course Piazza page. It is mandatory that you enrolled the piazza page.