CMPE462 - Machine Learning

Spring 2018

Instructor: Ali Taylan Cemgil
(Volunteer) Student TA: Riza Ozcelik
Bogazici University,
Department of Computer Engineering,
Istanbul, Turkey


Follow cmpe462 on twitter

Github site for project submissions

Jupyter Notebooks (in preperation)



7-8 Feb Introduction, Linear Algebra and Probability review
14-15 Feb Supervised Learning, Linear Regression
21-22 Feb Classification, Naive Bayes, k-nearest neighbors, decision trees, model evaluation
28 Feb 1 Mar Classification, Logistic Regression
7-8 Mar Optimization, Gradient Descent, Newton's method, Momentum
14-15 Mar Regularization, Feature selection, Support vector machines
21-22 Mar Artificial Neural Networks (ANNs)
28-29 Mar Deep Learning Frameworks, PyTorch, Automatic Differentiation
4-5 Apr Unsupervised Learning, k-means clustering, spectral clustering
11-12 Apr Dimensionality reduction, Singular Value Decomposition, Principal Component Analysis
Spring Break
25-26 Apr Matrix Decompositions, Nonnegative Matrix Factorization, Recommendation systems
2-3 May Exam
9-10 May Review and what next?

Reference Textbooks

  • Introduction to Applied Linear Algebra
    Stephen Boyd and Lieven Vandenberghe, 2017
    Online Book, to be published by the Cambridge University Press

  • Deep Learning
    Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016
    published by MIT Press
    Book website



  • % 30 Written Exam

  • % 40 Projects, Quizes,

  • % 30 Final Project and Poster Presentation

Total Credits