STAT 430

Spring 2017 All Classes

All Classes

Topics in Applied Statistics

SHOW FINAL EXAM SCHEDULE

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 spring 2017
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60247	Lecture-Discussion	1GR	9:30AM -10:50AM	TR	119 Materials Science & Eng Bld	Stepanov, A	Part of Term: 1 Date Range: 01/17/17-05/03/17 Credit: 4 hours Section Title: Stochastic Processes Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Stochastic Processes Description: A stochastic process is a random process that represents the evolution of some system over time. The course is aimed at advanced undergraduate and beginning graduate students. Topics include discrete-time Markov chains, random walks, continuous-time Markov chains, Poisson processes, birth-and-death processes, renewal processes, queues, Brownian motion (Wiener process), and Ito's lemma. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60249	Lecture-Discussion	1UG	9:30AM -10:50AM	TR	119 Materials Science & Eng Bld	Stepanov, A	Part of Term: 1 Date Range: 01/17/17-05/03/17 Credit: 3 hours Section Title: Stochastic Processes Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Stochastic Processes Description: A stochastic process is a random process that represents the evolution of some system over time. The course is aimed at advanced undergraduate and beginning graduate students. Topics include discrete-time Markov chains, random walks, continuous-time Markov chains, Poisson processes, birth-and-death processes, renewal processes, queues, Brownian motion (Wiener process), and Ito's lemma. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
36200	Lecture-Discussion	2GR	10:00AM -10:50AM	MWF	114 Transportation Building	Dalpiaz, D	Part of Term: 1 Date Range: 01/17/17-05/03/17 Credit: 4 hours Section Title: Basics of Statistical Learning Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Basics of Statistical Learning Description: This course introduces machine learning techniques for prediction, classification, and clustering. There is an emphasis on resampling methods in model building, especially cross validation. Topics include model selection, nonparametric regression, logistic regression, decision trees, support vector machines, dimension reduction and cluster analysis. Prerequisite: A course in linear regression, such as STAT 420 or STAT 425 Restriction(s): Restricted to Graduate - Urbana-Champaign.
36199	Lecture-Discussion	2UG	10:00AM -10:50AM	MWF	114 Transportation Building	Dalpiaz, D	Part of Term: 1 Date Range: 01/17/17-05/03/17 Credit: 3 hours Section Title: Basics of Statistical Learning Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Basics of Statistical Learning Description: This course introduces machine learning techniques for prediction, classification, and clustering. There is an emphasis on resampling methods in model building, especially cross validation. Topics include model selection, nonparametric regression, logistic regression, decision trees, support vector machines, dimension reduction and cluster analysis. Prerequisite: A course in linear regression, such as STAT 420 or STAT 425 Restriction(s): Restricted to Undergrad - Urbana-Champaign.
63951	Online	RB2	ARRANGED	n.a.	n.a.	Brunner, R	Part of Term: 1 Date Range: 01/17/17-05/03/17 Credit: 3 hours Section Title: Advanced Data Science Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Students must be registered for this course by 4 pm on Wednesday January 18, 2017. No new students will be allowed to register for this class after that. The materials and deadlines for this course can be found here: https://github.com/lcdm-uiuc/info490-sp17 Extensions to assignment deadlines will NOT be granted so make sure to check the site as soon as you register. TOPIC: Advanced Data Science Description: This class is an asynchronous, online course. Please see INFO 490 (section RB2, CRN 64015) for more information. This course will introduce advanced data science concepts by building on the foundational concepts presented in INFO 490: Foundations of Data Science. Students will first learn how to perform more statistical data exploration and constructing and evaluating statistical models. Next, students will learn machine learning techniques including supervised and unsupervised learning, dimensional reduction, and cluster finding. An emphasis will be placed on the practical application of these techniques to high-dimensional numerical data, time series data, image data, and text data. Finally, students will learn to use relational databases and cloud computing software components such as Hadoop, Spark, and NoSQL data stores. Students must have access to a fairly modern computer, ideally that supports hardware virtualization, on which they can install software. This class is open to sophomores, juniors, seniors and graduate students in any discipline who have either taken a previous INFO 490 data science course or have received instructor permission. Restrictions: Not intended for students with Freshman class standing. The STAT 430 section is restricted to statistics students only. Other students would register for INFO 490 section RB2, CRN 64015. Priority registration is restricted to students majoring in Statistics or Statistics & Computer Science. Students minoring in Statistics are expected to be added to the restrictions sometime during the business day December 1, 2016. Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s) or minor(s). Not intended for students with Freshman class standing.