Time & Place. Tue/Thu, 09:30AM-10:45AM, Stanley Thomas 302.
Instructor. Parisa Kordjamshidi (Office hours: Stanley Thomas 305 B, Tu/Th 10:45-12:30pm).
This course provides an introduction to machine learning to undergraduate students though it is open to a small number of graduate students.
Given the popularity of applying intelligent systems mostly including machine learning techniques to a large variety of real world problems and application domains, the goal of this course is to help experts in various fields to understand, design, evaluate and use Machine Learning in their major field of expertise.
This course has two parts which will be worked out in parallel. In one part of the course, we study a variety of learning algorithms and techniques that have proved successful in practical applications. We will discuss supervised and unsupervised techniques including decision trees, rule based learning, on-line learning, neural networks, support vector machines, probabilistic approaches and more as well as clustering techniques.
In the other part of the course, we study designing an end-to-end machine learning model when starting from raw data, working on preprocessing, extracting features, designing various model configurations, and evaluation. The students choose a programming language and software tools that they prefer, they will choose an application problem related to their major field of study, focus on one of the studied techniques and investigate it in depth. They will make a presentation about the problem and their solution, apply their selected technique and report their evaluation results.