STAT 430

Fall 2018 All Classes

All Classes

Topics in Applied Statistics

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Credit: 3 OR 4 hours.

Formulation and analysis of mathematical models for random phenomena; extensive involvement with the analysis of real data; and instruction in statistical and computing techniques as needed.

3 undergraduate hours. 4 graduate hours. May be repeated with approval. Prerequisite: STAT 410 or STAT 420; or consent of instructor.

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STAT 430 class schedule data for fall 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60255	Lecture-Discussion	1GR	8:00AM -9:20AM	TR	103 Transportation Building	Hua, L	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: MachineLearning Financial Data Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Machine Learning for Financial Data PREREQS: A course in linear regression, such as STAT 420 or STAT 425; and basic knowledge about classical machine learning techniques at the level of the book "An Introduction to Statistical Learning"; and basic skills in using R to implement machine learning algorithms and conduct data analysis; DESCRIPTION: This course introduces modern machine learning techniques that are tailored for analyzing financial data. Topics include Financial Data Preprocessing, Ensemble Methods, Cross Validation, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory / Gated Recurrent Units, Generative Adversarial Networks. The emphasis is on the basics of these methods and their relevant applications with financial data. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60257	Lecture-Discussion	1UG	8:00AM -9:20AM	TR	103 Transportation Building	Hua, L	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: MachineLearning Financial Data Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Machine Learning for Financial Data PREREQS: A course in linear regression, such as STAT 420 or STAT 425; and basic knowledge about classical machine learning techniques at the level of the book "An Introduction to Statistical Learning"; and basic skills in using R to implement machine learning algorithms and conduct data analysis; DESCRIPTION: This course introduces modern machine learning techniques that are tailored for analyzing financial data. Topics include Financial Data Preprocessing, Ensemble Methods, Cross Validation, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory / Gated Recurrent Units, Generative Adversarial Networks. The emphasis is on the basics of these methods and their relevant applications with financial data. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
55664	Lecture-Discussion	2GR	10:00AM -10:50AM	MWF	144 Loomis Laboratory		Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Section Pending Section Info: PENDING section may or may not be offered. Please plan your schedule as if this will NOT be offered. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: TBD Restriction(s): Restricted to Graduate - Urbana-Champaign.
55666	Lecture-Discussion	2UG	10:00AM -10:50AM	MWF	144 Loomis Laboratory		Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Section Pending Section Info: PENDING section may or may not be offered. Please plan your schedule as if this will NOT be offered. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: TBD Restriction(s): Restricted to Undergrad - Urbana-Champaign.