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 assistants are Gorkem Akyildiz and Sevginur Ince.
Lectures: Mondays at 11:40-12:30 (D2) and Tuesdays 13:40-15:30 (D9)
Tutorials: Tutorials: Wednesdays at 15:40-17:30 (D10)
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 ed. Please enroll it by following the link https://edstem.org/eu/join/nSWkR5 using your departmental account.
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 |
Sep 23 | 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 |
Sep 24 | Machine Learning by Examples, Nearest Neighbor Classifier [slides] | Reading: Barber 1,14.1-14.2 Demo: k-Nearest Neighbors |
Sep 30 | Kernel Regression, Distance Functions, Curse of Dimensionality [slides] | |
Oct 1 | Linear Regression, Generalization, Model Complexity, Regularization [slides] | Assg1 out Reading: Bishop 1.1, 3.1, Stanford CS229 note Demo: Curve fitting |
Oct 7 | Machine Learning Methodology [slides] | Reading: P. Domingos, A few useful things to know about machine learning |
Oct 8 | 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 14 | Statistical Estimation: MLE | |
Oct 15 | Statistical Estimation: MAP, Naïve Bayes Classifier | Assg1 due |
Oct 21 | Logistic Regression, Discriminant vs. Generative Classification | |
Oct 22 | Linear Discriminant Functions, Perceptron | Assg2 out |
Oct 28 | National Holiday (Republic Day) | |
Oct 29 | National Holiday (Republic Day) | Course project proposal due |
Nov 4 | Multi-layer Perceptron | |
Nov 5 | Training Neural Networks: Computational Graph, Back-propagation | Assg2 due |
Nov 11 | Introduction to Deep Learning | |
Nov 12 | Deep Convolutional Networks | |
Nov 18 | Midterm review | |
Nov 19 | Midterm exam | Assg3 out |
Nov 25 | Support Vector Machines (SVMs) | |
Nov 26 | Soft margin SVM, Multi-class SVM | |
Dec 2 | Kernels, Kernel Trick for SVMs, Support Vector Regression | |
Dec 3 | Decision Tree Learning | Assg3 due |
Dec 9 | Ensemble Methods: Bagging, Random Forests | |
Dec 10 | Ensemble Methods: Boosting | Project progress reports due |
Dec 16 | Clustering: K-Means, Spectral Clustering, Agglomerative Clustering | |
Dec 17 | Dimensionality Reduction: PCA, SVD, ICA, Autoencoders | |
Dec 23 | Project presentations | |
Dec 24 | Project presentations, Course wrap-up | Final project reports due |