CSCI 374
Machine Learning Fall 2022
Division III Quantative/Formal Reasoning

Class Details

Machine learning is a field that derives from artificial intelligence and statistics, and is concerned with the design and analysis of computer algorithms that “learn” automatically through the use of data. Computer algorithms are capable of discerning subtle patterns and structure in the data that would be practically impossible for a human to find. As a result, real-world decisions, such as treatment options and loan approvals, are being increasingly automated based on predictions or factual knowledge derived from such algorithms. This course explores topics in supervised learning (e.g., random forests and neural networks), unsupervised learning (e.g., k-means clustering and expectation maximization), and possibly reinforcement learning (e.g., Q-learning and temporal difference learning.) It will also introduce methods for the evaluation of learning algorithms (with an emphasis on analysis of generalizability and robustness of the algorithms to distribution/environmental shift), as well as topics in computational learning theory and ethics.
The Class: Format: lecture
Limit: 24
Expected: 24
Class#: 1275
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; the final two weeks are focused on a project of the student's design.
Prerequisites: CSCI 136 and CSCI 256 or permission of instructor
Enrollment Preferences: Current or expected Computer Science majors.
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: This course heavily relies on discrete mathematics, calculus, and elementary statistics. Students will be proving theorems, among many other mathematically oriented assignments. Additionally, they will be programming, which involves analytical and logical thinking.
Attributes: COGS Interdepartmental Electives

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