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 Sibel Kapan.
Lectures: Mondays at 14:00-14:50 and Wednesdays 10:00-11:50 (D1)
Tutorials: Tutorials: Thursdays at 16:00-18:00 (D9)
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 https://piazza.com/hacettepe.edu.tr/fall2022/ain311.
AIN311 is a mandatory course for third-year undergraduate students who enrolled in Artificial Intelligence Engineering program. The prerequisites for this course are:
Grading for AIN311 will be based on
Grading for AIN313 will be based on
Date | Topic | Notes |
Oct 3 | 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 |
Oct 5 | Machine Learning by Examples, Nearest Neighbor Classifier [slides] | Reading: Barber 1,14.1-14.2 Demo: k-Nearest Neighbors |
Oct 10 | Kernel Regression, Distance Functions, Curse of Dimensionality | |
Oct 12 | Linear Regression, Generalization, Model Complexity, Regularization | Assg1 out |
Oct 17 | Machine Learning Methodology | |
Oct 19 | Learning Theory, Basic Probability Review | |
Oct 24 | Statistical Estimation: MLE | |
Oct 26 | Statistical Estimation: MAP, Naïve Bayes Classifier | Assg1 due |
Oct 31 | Logistic Regression, Discriminant vs. Generative Classification | |
Nov 2 | Linear Discriminant Functions, Perceptron | Assg2 out |
Nov 7 | Multi-layer Perceptron | Course project proposal due |
Nov 9 | Training Neural Networks: Computational Graph, Back-propagation | |
Nov 14 | Introduction to Deep Learning | |
Nov 16 | Deep Convolutional Networks | Assg2 due |
Nov 21 | Support Vector Machines (SVMs) | |
Nov 23 | Soft margin SVM, Multi-class SVM | |
Nov 28 | Midterm review | |
Nov 30 | Midterm exam | Assg3 out |
Dec 5 | Kernels, Kernel Trick for SVMs, Support Vector Regression | |
Dec 7 | Decision Tree Learning | |
Dec 12 | Ensemble Methods: Bagging, Random Forests | |
Dec 14 | Ensemble Methods: Boosting | Assg3 due |
Dec 19 | Clustering: K-Means | Project progress reports due |
Dec 21 | Clustering: Spectral Clustering, Agglomerative Clustering | |
Dec 26 | Dimensionality Reduction: PCA, SVD | |
Dec 28 | Dimensionality Reduction: ICA, Autoencoders | |
Jan 2 | Project presentations | |
Jan 4 | Project presentations (cont'd.), Course wrap-up | Final project reports due |