Probability

Spring 2025
#22831

Concepts of Probability

Adam R Lucas
Jan 21, 2025 - May 09, 2025
Mo, We, Fr
10:00 am - 10:59 am

Instruction Mode: In-Person Instruction

Open Seats

STAT 134 - LEC 001 Concepts of Probability more detail
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
Spring 2025
#29359

Probability and Risk Analysis for Engineers

Runhan Xie, William Yan
Jan 21, 2025 - May 09, 2025
Fr
02:00 pm - 02:59 pm
Social Sciences Building 20

Instruction Mode: In-Person Instruction

Open Seats

6 Unreserved Seats

INDENG 172 - DIS 101 Probability and Risk Analysis for Engineers more detail
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
Spring 2025
#29358

Probability and Risk Analysis for Engineers

Daniel Pirutinsky
Jan 21, 2025 - May 09, 2025
Tu, Th
03:30 pm - 04:59 pm

Instruction Mode: In-Person Instruction

Open Seats

INDENG 172 - LEC 1 Probability and Risk Analysis for Engineers more detail
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
Spring 2025
#29530

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
12:00 am

Instruction Mode: In-Person Instruction

Open Seats

40 Unreserved Seats

EECS 126 - DIS 999 Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29277

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
Th
08:00 am - 08:59 am

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open Seats
EECS 126 - DIS 108D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29108

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
We
01:00 pm - 01:59 pm

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open Seats
EECS 126 - DIS 107D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29105

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
Fr
11:00 am - 11:59 am

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open Seats
EECS 126 - DIS 106D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29091

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
Fr
02:00 pm - 02:59 pm

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open Seats
EECS 126 - DIS 105D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29090

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
We
04:00 pm - 04:59 pm

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

32 Unreserved Seats

EECS 126 - DIS 104D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Spring 2025
#29059

Probability and Random Processes

Jan 21, 2025 - May 09, 2025
We
03:00 pm - 03:59 pm

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open Seats
EECS 126 - DIS 103D Probability and Random Processes more detail
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.