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 11:30-12:30 (D4) and Wednesdays 09:40-11:30 (D1)
Tutorials: Tutorials: Fridays at 16:40-18:30 (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/KXqbx8 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 |
Oct 2 | 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 4 | Machine Learning by Examples, Nearest Neighbor Classifier [slides] | Reading: Barber 1,14.1-14.2 Demo: k-Nearest Neighbors |
Oct 9 | Kernel Regression, Distance Functions, Curse of Dimensionality [slides] | |
Oct 11 | Linear Regression, Generalization, Model Complexity, Regularization [slides] | Assg1 out Reading: Bishop 1.1, 3.1, Stanford CS229 note Demo: Curve fitting |
Oct 16 | Machine Learning Methodology [slides] | Reading: P. Domingos, A few useful things to know about machine learning |
Oct 18 | 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 23 | Statistical Estimation: MLE [slides] | Reading: Murphy 2.1-2.3.2 Video: Daphne Koller, MLE Lecture, MAP Lecture |
Oct 25 | 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 30 | 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 |
Nov 1 | Linear Discriminant Functions, Perceptron [slides] | Assg2 out Reading: Bishop 4.1.1-4.1.2, 4.5, Daume III 3 |
Nov 6 | Multi-layer Perceptron [slides] | Course project proposal due |
Nov 8 | Training Neural Networks: Computational Graph, Back-propagation [slides] | Reading: CS 231 Backpropagation notes Demo: A Neural Network Playground |
Nov 13 | Introduction to Deep Learning [slides] | Reading: Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton |
Nov 15 | Deep Convolutional Networks [slides] | Assg2 due Reading: Conv Nets: A Modular Perspective, Understanding Convolutions, Christopher Olah |
Nov 20 | Support Vector Machines (SVMs) [slides] | Reading: Alpaydin 13.1-13.2 Video: Patrick Winston, Support Vector Machines |
Nov 22 | Soft margin SVM, Multi-class SVM [slides] | Reading: Alpaydin 13.3, 13.9, M.A. Hearst, Support Vector Machines, CS229 Notes 3.7 Demo: Multi-class SVM demo |
Nov 27 | Midterm review | |
Nov 29 | Midterm exam | Assg3 out |
Dec 4 | Kernels, Kernel Trick for SVMs, Support Vector Regression [slides] | Reading: 13.5-13.7, 13.10 |
Dec 6 | Decision Tree Learning [slides] | Reading: Mitchell 3, Bishop 14.4 Demo: A Visual Introduction to Machine Learning |
Dec 11 | 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 13 | Ensemble Methods: Boosting [slides] | Assg3 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 18 | Clustering: K-Means [slides] | Project progress reports due Reading: Bishop 9.1 Cluster Analysis: Basic Concepts and Algorithms, Pang-Ning Tan, Michael Steinbach and Vipin Kumar Demo: Visualizing K-Means equilibria |
Dec 20 | Clustering: Spectral Clustering, Agglomerative Clustering [slides] | |
Dec 25 | Dimensionality Reduction: PCA, SVD [slides] | |
Dec 27 | Dimensionality Reduction: ICA, Autoencoders [slides] | |
Jan 1 | No class - The first day of the year | |
Jan 3 | Project presentations, Course wrap-up | Final project reports due |