BBM 406-Fundamentals of Machine Learning

Fall 2025

Instructor: Assoc. Prof. Dr. Hacer Yalım Keleş

Email: hacerkeles@cs.hacettepe.edu.tr

Office Hours: Thursday, 15:40-17:30

Lectures: Thursday, 12:40-15:30

Room: Seminar Hall (SH)

Communication: Course Piazza Page

Reference Textbooks:

Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer (2006) (PRML: available online)

Supplementary Textbooks:

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.)

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 with the complementary course BBM409.

Course Content:

Classification: perceptrons, support vector machines (SVMs), Gaussian discriminant analysis (linear discriminant analysis, LDA, and quadratic discriminant analysis, QDA), logistic regression, decision trees, neural networks, convolutional neural networks, boosting, nearest neighbor search;
Regression: least-squares linear regression, polynomial regression
Density estimation: maximum likelihood estimation (MLE);
Dimensionality reduction: principal components analysis (PCA)
Clustering: k-means and soft k-means clustering.

Tentative Course Schedule

Week#	Date	Subject	BBM409
1	25-Sep-2025	Introduction
2	2-Oct-2025	ML Basics with Linear Regression	Tutorial #1
3	9-Oct-2025	Linear Classifiers	Tutorial #2
4	16-Oct-2025	Perceptron Learning Algorithm, Fisher's Linear Discriminant	Tutorial #3 Q1
5	23-Oct-2025	Logistic Regression, Multi-class Classification	PA1
6	30-Oct-2025	Support Vector Machines (Hard and Soft Margin), Kernel Trick.	Q2
7	6-Nov-2025	Decision trees, Ensemble Learning.	PA2
8	13-Nov-2025	Midterm Exam I	-
9	20-Nov-2025	Feed Forward Neural Networks, Backpropagation Algorithm	PA3
10	27-Nov-2025	Convolutional Neural Networks, CNN Architectures	Q3
11	4-Dec-2025	Recurrent Neural Networks	PA4
12	11-Dec-2025	Bayesian Decision Theory, Naive Bayes Algorithm. Discriminant Analysis of Normal Density.	Q4
13	18-Dec-2025	Parametric Density Estimation (MLE), Nonparametric Density Estimation (KDE, kNN).
14	25-Dec-2025	Unsupervised Learning: PCA, Clustering, Autoencoders	Q5

Assessment:

Quizes: 20%

Midterm Exam I: 30%

Final Exam: 50%

Logistics:

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

BBM 409: Machine Learning Lab

Schedule: Tuesday, 16:40-18:30.

TA1: Sümeyye Meryem Taşyürek (meryemtasyurek@cs.hacettepe.edu.tr)

TA2: Sevginur İnce (sevginurince@cs.hacettepe.edu.tr)

Tentative Assessment:

Assignment-1: 15%

Assignment-2: 25%

Assignment-3: 30%

Assignment-4: 30%

Logistics:

All communication and announcements will be posted in the Piazza page: BBM409 Piazza Page

It is mandatory that you enrolled the piazza using this link.

Policy:

Turning in someone else’s work, in whole or in part, as your own will be considered as a violation of academic integrity.