Course Information

About

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 Aykut Erdem - the teaching assistant is Necva Bolucu.

        

Time and Location

Lectures: Thursdays at 16:00-16:50 and Fridays 09:00-10:50 (Room D9)
Tutorials: Tutorials: Fridays at 13:30-15:30 (Room D8)

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.

Communication

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/fall2018/bbm406.

Pre-requisites

BBM406 is open to third/fourth-year undergraduate and M.Sc. 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

Schedule

Date Topic Notes
Oct 11 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
Oct 12 Machine Learning by Examples, Nearest Neighbor Classifier [slides] Reading: Barber 1,14.1-14.2
Demo: k-Nearest Neighbors
Tutorial: Python/numpy
Oct 18 Kernel Regression, Distance Functions, Curse of Dimensionality [slides] Reading: Bishop 1.4, 2.5
Oct 19 Linear Regression, Generalization, Model Complexity, Regularization Assg1 out
Reading: Bishop 1.1, 3.1, Stanford CS229 note
Demo: Linear regression
Oct 25 Machine Learning Methodology Reading: P. Domingos, A few useful things to know about machine learning
Oct 26 Learning Theory, Basic Probability Review 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
Nov 1 Statistical Estimation: MLE and MAP Reading: Murphy 2.1-2.3.2
Video: Daphne Koller, Probabilistic Graphical Models, MLE Lecture, MAP Lecture
Nov 2 Statistical Estimation: MLE and MAP (cont'd.), Naïve Bayes Classifier 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
Nov 8 Logistic Regression, Discriminant vs. Generative Classification Reading: Barber 17.4, Bishop 4.1.1-4.1.2
Optional Reading: On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, Andrew Y. Ng, Michael I. Jordan
Nov 9 Linear Discriminant Functions, Perceptron Assg2 out
Reading: Bishop 4.5, Daume III 3
Nov 15 Multi-layer Perceptron Video: Neural Networks, Andrew Ng
Nov 16 Training Neural Networks: Computational Graph, Back-propagation Course project proposal due
Reading: CS 231 Backpropagation notes
Demo: A Neural Network Playground
Nov 22 Introduction to Deep Learning Reading: Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton
Nov 23 Deep Convolutional Networks Assg2 due
Reading: Conv Nets: A Modular Perspective, Understanding Convolutions, Christopher Olah
Nov 29 Midterm review
Nov 30 Midterm exam Assg3 out
Dec 6 Support Vector Machines (SVMs) Reading: Alpaydin 13.1-13.2
Video: Patrick Winston, Support Vector Machines
Demo: Andrea Vedaldi's SVM MATLAB demo
Dec 7 Soft margin SVM, Multi-class SVM Reading: Alpaydin 13.3, 13.9, M.A. Hearst, Support Vector Machines, CS229 Notes 3.7
Demo: Multi-class SVM demo
Dec 13 Kernels, Kernel Trick for SVMs, Support Vector Regression Reading: 13.5-13.7, 13.10
Dec 14 Decision Tree Learning Assg3 due
Reading: Mitchell 3, Bishop 14.4
Demo: A Visual Introduction to Machine Learning
Dec 20 Ensemble Methods: Bagging, Random Forests 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 21 Ensemble Methods: Boosting 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 27 Clustering: K-Means Reading: Bishop 9.1
Cluster Analysis: Basic Concepts and Algorithms, Pang-Ning Tan, Michael Steinbach and Vipin Kumar
Demo: Visualizing K-Means equilibria
Dec 28 Clustering: Spectral Clustering, Agglomerative Clustering
Jan 3 Dimensionality Reduction: Principle Component Analysis, Singular Value Decomposition Reading: Barber 15.1-15.3, 15.7, Stanford CS229 note
Video: PCA, Andrew Ng
Demo: Principal Component Analysis Explained Visually
Jan 4 Dimensionality Reduction: Autoencoders, Independent Component Analysis
Jan 10 Project presentations
Jan 11 Project presentations (cont'd.), Course wrap-up Final project reports due

Resources

Related Conferences

  • Advances in Neural Information Processing Systems (NIPS)
  • 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