STAT 430

Spring 2016 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.

My Favorites

My Schedule

Section Status updates every 10 minutes.

STAT 430 class schedule data for spring 2016
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60247	Lecture-Discussion	1GR	2:00PM -3:20PM	TR	120 Architecture Building	Dalpiaz, D	Part of Term: 1 Date Range: 01/19/16-05/04/16 Credit: 4 hours Section Title: Basics of Statistical Learning Section Info: 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. A course in linear regression, such as STAT 420 or STAT 425, is a prerequisite. Initially limited to statistics graduate students and seniors. Late Tuesday morning, November 24th, statistics juniors will also have access to any open seats. Late Wednesday morning, November 25th, all statistics students will have access to any open seats. Priority registration is restricted to Statistics graduate students. This restriction is expected to be removed sometime during the business day December 1, 2015. This course is set below capacity to hold space for incoming Statistics graduate students. If you are one of those students, please speak to your advisor. Capacity may fluctuate. If the remaining seats are at 0 or negative, you will not be able to register. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60249	Lecture-Discussion	1UG	2:00PM -3:20PM	TR	120 Architecture Building	Dalpiaz, D	Part of Term: 1 Date Range: 01/19/16-05/04/16 Credit: 3 hours Section Title: Basics of Statistical Learning Section Info: 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. A course in linear regression, such as STAT 420 or STAT 425, is a prerequisite. Initially limited to statistics graduate students and seniors. Late Tuesday morning, November 24th, statistics juniors will also have access to any open seats. Late Wednesday morning, November 25th, all statistics students will have access to any open seats. Priority registration is restricted to students majoring in Statistics or Statistics & Computer Science. This restriction is expected to be removed sometime during the business day December 1, 2015. This course is set below capacity to hold space for students transferring into Statistics. If you are one of those students, please speak to your advisor. Capacity may fluctuate. If the remaining seats are at 0 or negative, you will not be able to register. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
36200	Lecture-Discussion	2GR	2:00PM -2:50PM	MWF	1002 Lincoln Hall	Stepanov, A	Part of Term: 1 Date Range: 01/19/16-05/04/16 Credit: 4 hours Section Title: Stochastic Processes Section Info: 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. Initially limited to statistics graduate students and seniors. Late Tuesday morning, November 24th, statistics juniors will also have access to any open seats. Late Wednesday morning, November 25th, all statistics students will have access to any open seats. Priority registration is restricted to Statistics graduate students. This restriction is expected to be removed sometime during the business day December 1, 2015. This course is set below capacity to hold space for incoming Statistics graduate students. If you are one of those students, please speak to your advisor. Capacity may fluctuate. If the remaining seats are at 0 or negative, you will not be able to register. Restriction(s): Restricted to Graduate - Urbana-Champaign.
36199	Lecture-Discussion	2UG	2:00PM -2:50PM	MWF	1002 Lincoln Hall	Stepanov, A	Part of Term: 1 Date Range: 01/19/16-05/04/16 Credit: 3 hours Section Title: Stochastic Processes Section Info: 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. Initially limited to statistics graduate students and seniors. Late Tuesday morning, November 24th, statistics juniors will also have access to any open seats. Late Wednesday morning, November 25th, all statistics students will have access to any open seats. Priority registration is restricted to students majoring in Statistics or Statistics & Computer Science. This restriction is expected to be removed sometime during the business day December 1, 2015. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
63951	Online	RB2	ARRANGED	n.a.	n.a.	Brunner, R	Part of Term: 1 Date Range: 01/19/16-05/04/16 Credit: 3 hours Section Title: Advanced Data Science Section Info: This class is an asynchronous, online course. Please see INFO 490 (section RB2, CRN 64015) for more information. Advanced Data Science: 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. Restriction(s): Not intended for students with Freshman class standing. The STAT 430 section is restricted to Statistics students only. Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s). Not intended for students with Freshman class standing.