Advanced Machine Learning, Data Mining, and Artificial Intelligence

CSCI E-82

Section 1

CRN 15407

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The course is intended to combine the theory with the hands-on practice of solving modern industry problems with an emphasis on image processing and natural language processing. Topics include outlier detection, advanced clustering techniques, deep learning, dimensionality reduction methods, frequent item set mining, and recommender systems. Topics also considered include reinforcement learning, graph-based models, search optimization, and time series analysis. The course uses Python as the primary language, although later projects can include R and other languages. The course also introduces some industry standard tools to prepare students for artificial intelligence jobs.

Credits: 4

Part of Term

Full Term

Format

Live Attendance Web Conference

Credit Status

Graduate, Noncredit, Undergraduate

Section Status

Open

Instructor Info

Peter V. Henstock, PhD

Machine Learning and Artificial Intelligence Lead, AI Institute, Incyte

Meeting Info

F 7:40pm - 9:40pm (8/31 - 12/19)

Participation Option: Online Synchronous

Deadlines

Last day to register:

Additional Time Commitments

Optional sections to be arranged.

Prerequisites

This course builds upon topics covered in CSCI E-63c and CSCI E-109a with either CSCI E-63c or CSCI E-109a as a prerequisite. Students should be proficient in Python including Pandas and readily able to load, parse, and manipulate data. A course such as CSCI E-7 or a course on Python and machine learning would be useful.

Notes

This course meets via web conference. Students must attend and participate at the scheduled meeting time. See minimum technology requirements.

Syllabus

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Tuition

All Sections of this Course

CRN	Section #	Participation Option(s)	Instructor	Section Status	Meets	Term Dates
15407	1	Online Synchronous	Peter Henstock	Open	F 7:40pm - 9:40pm	Aug 31 to Dec 19