CSCI 374
Machine Learning Spring 2020
Division III Quantative/Formal Reasoning

Class Details

This tutorial examines the design, implementation, and analysis of machine learning algorithms. Machine Learning is a branch of Artificial Intelligence that aims to develop algorithms that will improve a system’s performance. Improvement might involve acquiring new factual knowledge from data, learning to perform a new task, or learning to perform an old task more efficiently or effectively. This tutorial will cover examples of supervised learning algorithms (including decision tree learning, support vector machines, and neural networks), unsupervised learning algorithms (including k-means and expectation maximization), and possibly reinforcement learning algorithms (such as Q learning and temporal difference learning). It will also introduce methods for the evaluation of learning algorithms, as well as topics in computational learning theory.
The Class: Format: tutorial
Limit: 10
Expected: 10
Class#: 3671
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation: presentations, problem sets, programming exercises, empirical analyses of algorithms, critical analysis of current literature
Prerequisites: CSCI 136 and CSCI 256 or permission of instructor
Enrollment Preferences: Computer Science majors
Distributions: Division III Quantative/Formal Reasoning
Attributes: COGS Interdepartmental Electives

Class Grid

Updated 6:10 am ET

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