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 Burcak Asal.


Time and Location

Lectures: Tuesdays at 13:00-13:50 and Thursdays 13:00-14:50 (Zoom)
Tutorials: Tutorials: Thursdays 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


AIN311 is a mandatory course for third-year undergraduate students who enrolled in Artificial Intelligence Engineering program. The prerequisites for this course are:

Course Requirements and Grading

Grading for AIN311 will be based on

Grading for AIN313 will be based on


Date Topic Notes
Sep 28 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
Sep 30 Machine Learning by Examples, Nearest Neighbor Classifier [slides] Reading: Barber 1,14.1-14.2
Demo: k-Nearest Neighbors
Oct 5 Kernel Regression, Distance Functions, Curse of Dimensionality [slides]
Oct 7 Linear Regression, Generalization, Model Complexity, Regularization [slides] Assg1 out
Reading: Bishop 1.1, 3.1, Stanford CS229 note
Demo: Curve fitting
Oct 12 Machine Learning Methodology [slides] Reading: P. Domingos, A few useful things to know about machine learning
Oct 14 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
Oct 19 Statistical Estimation: MLE [slides] Reading: Murphy 2.1-2.3.2
Video: Daphne Koller, Probabilistic Graphical Models, MLE Lecture, MAP Lecture
Oct 21 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
Oct 26 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
Oct 28 No class
Nov 2 Project discussion
Nov 4 Linear Discriminant Functions, Perceptron [slides] Assg2 out
Reading: Bishop 4.1.1-4.1.2, 4.5, Daume III 3
Nov 9 Multi-layer Perceptron [slides] Course project proposal due
Reading: Bishop Ch. 5.1
Video: Neural Networks, Andrew Ng
Nov 11 Training Neural Networks: Computational Graph, Back-propagation [slides]
Reading: CS 231 Backpropagation notes
Demo: A Neural Network Playground
Nov 16 Introduction to Deep Learning [slides] Reading: Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton
Nov 18 Deep Convolutional Networks [slides] Assg2 due
Reading: Conv Nets: A Modular Perspective, Understanding Convolutions, Christopher Olah
Nov 23 Support Vector Machines (SVMs) [slides] Reading: Alpaydin 13.1-13.2
Video: Patrick Winston, Support Vector Machines
Nov 25 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
Nov 30 Midterm review
Dec 2 Midterm exam
Dec 7 Kernels, Kernel Trick for SVMs, Support Vector Regression [slides] Reading: 13.5-13.7, 13.10
Dec 9 Decision Tree Learning [slides] Assg3 due
Reading: Mitchell 3, Bishop 14.4
Demo: A Visual Introduction to Machine Learning
Dec 14 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
Dec 16 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
Dec 21 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
Dec 23 Clustering: Spectral Clustering, Agglomerative Clustering [slides]
Dec 28 Dimensionality Reduction: PCA, SVD, ICA, Autoencoders [slides] Reading: Bishop 12.1 Stanford CS229 note
Optional Reading: Eigenfaces for Recognition, Matthew Turk and Alex Pentland
Video: PCA, Andrew Ng
Demo: Principal Component Analysis Explained Visually
Dec 30 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