Course Emphasis and Goals

The goal of machine learning is to build computer systems that automatically solve problems using sample data and past experience. This course provides an overview of the state-of-art algorithms along with their theoretical and practical aspects. In this course, students will have an opportunity to have hands-on experience to understand the basic principles of these machine learning algorithms.

Description

Bayesian decision theory, parametric methods, nonparametric methods, decision trees, linear discrimination, ,multilayer perceptrons, unsupervised learning and clustering, hidden Markov models, and reinforcement learning.

Prerequisites

• Familiarity with the basic probability theory, statistics, and artificial intelligence.
• Knowledge of a programming language(Java, C/C++, Matlab, etc.) to write reasonably non-trivial programs.

Grading Policy

Homework: 20%
Midterm: 35%
Final: 45%

Exams

The exams will be closed book. You may bring only 2 A4 sheets (back and front = 4 pages) of your handwritten notes to the exam.

Homework Assignments and Late Policy

Homeworks will be programming assignments that require students to implement machine learning algorithms. Students are expected to work in groups of two for the assignments. For each assignment, students will be expected to write a detailed report on their findings by running their algorithms on given data sets.

Assignments are expected to be turned in by 17:00 on the due date. For the late assignments, each group will be given a total of three grace days (whole or partial) for the whole semester. Once these late days have been exhausted, no late assignments will be accepted. As an example, if Group A submits their 1st assignment 29 hours late, they will have used two late days and have only one day left. If Group A then submits their 3rd assignment 5 hours late, they will have used their remaining late day. If Group A submits their 4th assignment 1 minute late, this assignment will not be accepted.

Academic Integrity

Copying or communicating during an exam is cheating. Students caught cheating on an exam will be subject to disciplinary action, as explained in the "Student Disciplinary Rules and Regulation" ( http://www.provost.bilkent.edu.tr/procedures/AcademicHonesty.htm).

Students in the same group are expected to work together. On the other hand, students in different groups are not allowed to discuss the solutions of programming assignments or to get help to write their codes and reports. Students caught cheating on assignments will also be subject to disciplinary action.