ISE789/OR791 Optimization Models for Systems Analytics

ISE 789/OR 791: Optimization Models for Systems Analytics

The Use of Binary Choice Forests to Model and Estimate Discrete Choices
Sparse Signal Reconstruction via the Approximations of L₀ Quasinorm
Sparse Solutions by a Quadratically Constrained L_q (0<q<1) Minimization Model
A Gradient Descent Based Algorithm for L_p Minimization
A Unified Smooth Framework For Nonconvex Penalized Least Squares Problems
Optimization for Deep Learning: an Overview
Simple and Fast Iterative Algorithm for (Binary Integer) Online Linear Programming
How Can Machine Learning and Optimization Help Each Other Better?
Deep Learning in Business Analytics and Operations Research: Models, Applications and Managerial Implications
Customer Choice Models versus Machine Learning: Finding Optimal Product Displays on Alibaba
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
A Sub-one Quasi-norm-based Similarity Measure for Collaborative Filtering in Recommender Systems
DC Programming: The Optimization Method You Never Knew You Had to Know
Sparse Optimization Lecture: Sparse Recovery Guarantees
Convex Analysis and Duality over Discrete Domains
Using Radial Basis Function Networks for Function Approximation and Classification
Relaxed conditions for radial-basis function networks to be universal approximators
The Turbulent Past and Uncertain Future of AI
Covariance matrix
k-means clustering
Quadratically Constrained Quadratic Programming

Linear Conic Programming Theory and Applications Lecture 1 | Lecture 2 | Lecture 3 | Lecture 4 | Lecture 5

Assignment #1 Due:

Assignment #2 Due:

Assignment #3 Due:

Assignment #4 Due:

Assignment #5 Due:

Project Description

Final Exam: