MACHINE LEARNING
Session
Academic Term
Class Number
25874
Career
Graduate
Dates
1/19/2021 - 5/1/2021
Units
3 units
Grading
Grad LG/SNC Basis
Description
This course will give an overview of many techniques and algorithms in machine learning, beginning with topics such as linear and logistic regression, multi-layer neural networks and ending up with more recent topics such as boosting and support vector machines. The basic ideas and intuition behind modern machine learning methods, as well as, a more formal understanding of how and why they work will be covered. Students will have an opportunity to experiment with various machine learning techniques or apply them to a selected problem or domain in the context of a term project.
Enrollment Requirements
PLAN: Computer Science (CS-PHD; CS-MS; CSMSBS-MS) or Computer Engineering (COEAS-PHD; COEAS-MS; COEENG-PHD; COEENG-MCO)
Class Notes
The planned operational mode for this class when meeting in-person is FULL COHORT. For more information please visit http://www.provost.pitt.edu/students/student-success-flexpitt/flex-pitt-guarded-risk-posture-full-cohort-classroom/.

Class Details
Instructor(s)
Milos Hauskrecht
Meets
TuTh 1:15PM - 2:30PM
Meeting Dates
01/19/2021 - 05/01/2021
Room
2700 Wesley W Posvar Hall
Campus
Pittsburgh Campus
Location
Pittsburgh Campus
Components
Lecture Required
Textbooks/Materials
Textbooks to be determined
Enrollment Restrictions
CS Grads (MS or PHD)-1st Year
Available Seats: 0
CS MS/PHD- Year 2 or greater
Available Seats: 20
SCI MS/PHD programs
Available Seats: 4
Class Availability
Status
Open
Seats Taken
31
Seats Open
9
Combined Section Capacity
40
Unrestricted Seats
0
Restricted Seats
0
Wait List Total
0
Wait List Capacity
198
Combined Section
MACHINE LEARNING
ISSP 2170 - 1030 (10605)
Status: Open
Seats Taken: 2
Wait List Total: 0
MACHINE LEARNING
CS 2750 - 1010 (25874)
Status: Open
Seats Taken: 29
Wait List Total: 0