Course Information


This is a undergraduate-level introductory course in machine learning (ML) which will give a broad overview of many concepts and algorithms in ML, ranging from supervised learning methods such as support vector machines and decision trees, to unsupervised learning (clustering and factor analysis). The goal is to provide students with a deep understanding of the subject matter and skills to apply these concepts to real world problems. The course is taught by Erkut Erdem - the teaching assistant is Necva Bolucu.


Time and Location

Lectures: Mondays at 16:00-16:50 and Wednesdays 09:00-10:50 (Zoom)
Tutorials: Tutorials: Fridays at 16:00-18:00 (Zoom)

Reference Books

Policies: All work on assignments must be done individually unless stated otherwise. You are encouraged to discuss with your classmates about the given assignments, but these discussions should be carried out in an abstract way. That is, discussions related to a particular solution to a specific problem (either in actual code or in the pseudocode) will not be tolerated.

In short, turning in someone else’s work, in whole or in part, as your own will be considered as a violation of academic integrity. Please note that the former condition also holds for the material found on the web as everything on the web has been written by someone else.


The course webpage will be updated regularly throughout the semester with lecture notes, presentations, assignments and important deadlines. All other course related communications will be carried out through Piazza. Please enroll it by following the link


BBM406 is open to third/fourth-year undergraduate students. Non-CENG graduate students should ask the course instructor for approval before the add/drop period. The prerequisites for this course are:

Course Requirements and Grading

Grading for BBM406 will be based on

Grading for BBM409 will be based on


Date Topic Notes
Feb 22 Course outline and logistics, An overview of Machine Learning [slides] Reading: The Discipline of Machine Learning, Tom Mitchell
Video 1: The Master Algorithm, Pedro Domingos
Video 2: The Thinking Machine
Tutorial: Python/numpy
Feb 24 Machine Learning by Examples, Nearest Neighbor Classifier [slides] Reading: Barber 1,14.1-14.2
Demo: k-Nearest Neighbors
Mar 1 Kernel Regression, Distance Functions, Curse of Dimensionality [slides]
Mar 3 Linear Regression, Generalization, Model Complexity, Regularization [slides] Assg1 out
Reading: Bishop 1.1, 3.1, Stanford CS229 note
Demo: Curve fitting
Mar 8 Machine Learning Methodology [slides] Reading: P. Domingos, A few useful things to know about machine learning
Mar 10 Learning Theory, Basic Probability Review [slides] Reading: Daume III 12, Barber 1.1-1.4, CIS 520 note
E. Simoncelli, A Geometric Review of Linear Algebra
Video: Probability Primer
Demo: Seeing Theory: A visual introduction to probability and statistics
Mar 15 Statistical Estimation: MLE [slides] Reading: Murphy 2.1-2.3.2
Video: Daphne Koller, Probabilistic Graphical Models, MLE Lecture, MAP Lecture
Mar 17 Statistical Estimation: MAP, Naïve Bayes Classifier [slides] Assg1 due
Reading: Daume III 7, Naïve Bayes, Tom M. Mitchell
Optional Reading: Learning to Decode Cognitive States from Brain Images, Tom M. Mitchell et al.
Demo: Bayes Theorem
Mar 22 Logistic Regression, Discriminant vs. Generative Classification [slides] Reading: SLP3 5
Optional Reading: On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, Andrew Y. Ng, Michael I. Jordan
Mar 24 Linear Discriminant Functions, Perceptron [slides] Assg2 out
Reading: Bishop 4.1.1-4.1.2, 4.5, Daume III 3
Mar 29 Multi-layer Perceptron [slides] Video: Neural Networks, Andrew Ng
Mar 31 Training Neural Networks: Computational Graph, Back-propagation [slides] Course project proposal due
Reading: CS 231 Backpropagation notes
Demo: A Neural Network Playground
Apr 5 Introduction to Deep Learning [slides] Reading: Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton
Apr 7 Deep Convolutional Networks [slides] Assg2 due
Reading: Conv Nets: A Modular Perspective, Understanding Convolutions, Christopher Olah
Apr 12 Support Vector Machines (SVMs) [slides] Reading: Alpaydin 13.1-13.2
Video: Patrick Winston, Support Vector Machines
Apr 14 Soft margin SVM, Multi-class SVM [slides] Assg3 out
Reading: Alpaydin 13.3, 13.9, M.A. Hearst, Support Vector Machines, CS229 Notes 3.7
Demo: Multi-class SVM demo
Apr 19 Midterm review
Apr 21 Midterm exam
Apr 26 Kernels, Kernel Trick for SVMs, Support Vector Regression [slides] Reading: 13.5-13.7, 13.10
Apr 28 Decision Tree Learning [slides] Assg3 due
Reading: Mitchell 3, Bishop 14.4
Demo: A Visual Introduction to Machine Learning
May 3 Ensemble Methods: Bagging, Random Forests [slides] Reading: Bishop 14.1-14.2, Understanding the Bias-Variance Tradeoff, Scott Fortmann-Roe, Random Forests, Leo Breiman and Adele Cutler
Optional Reading: Real-Time Human Pose Recognition in Parts from Single Depth Images, Jamie Shotton et al.
Demo: Bootstrapping
May 5 Ensemble Methods: Boosting [slides] Project progress reports due
Reading: Bishop 14.3
Optional Reading: Rapid Object Detection using a Boosted Cascade of Simple Features, Paul Viola and Michael Jones
Video: A Boosting Tutorial, Robert Schapire
May 10 Clustering: K-Means [slides] Reading: Bishop 9.1
Cluster Analysis: Basic Concepts and Algorithms, Pang-Ning Tan, Michael Steinbach and Vipin Kumar
Demo: Visualizing K-Means equilibria
May 12 No class
May 17 Clustering: Spectral Clustering, Agglomerative Clustering [slides]
May 19 No class
May 24 Dimensionality Reduction: PCA, SVD, ICA, Autoencoders
May 26 Project presentations, Course wrap-up Final project reports due


Related Conferences

  • Advances in Neural Information Processing Systems (NeurIPS)
  • International Conference on Machine Learning (ICML)
  • The Conference on Uncertainty in Artificial Intelligence (UAI)
  • International Conference on Artificial Intelligence and Statistics (AISTATS)
  • IEEE International Conference on Data Mining (ICDM)

Related Journals

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Journal of Machine Learning Research
  • Data Mining and Knowledge Discovery
  • IEEE Transactions on Neural Networks

Python Resources

Linear Algebra

Resources for scientific writing and talks