CS 598 : Theoretical Machine Learning

CS 598 : Theoretical Machine Learning

In the last lecture, we were looking at clustering sparse graphs in the stochastic block model with parameters p > q, where p = a n , and q = b n , we saw that we could not do perfect clustering as w.h.p, there will be a constant fraction of isolated vertices. However, we shall see that we can still do weak recovery, i.e., by allowing a small fraction of vertices to be misplaced, we will recove...

CS 598 : Theoretical Machine Learning

1 Online decision making Online decision making is a generalization of the setting we studied in the previous lecture. In particular, our goal is to design an algorithm that can pick an action or make a decision at every time step from set of actions/decisions so that the loss/regret of the algorithm is not much worse than that of the best action or decision on the sequence. Here are some scena...

CS 598 : Theoretical Machine Learning

In the previous class, we had looked at the RMW algorithm. In this setting, the actions/decisions are referred to as experts. Assume that we have N experts. On any given day t, we choose an expert to use from among the N experts (like choosing a function h ∈ H). The adversary/world returns a loss vector lt = (lt 1, · · · lt N ). If the expert it is chosen on day t, then the performance after T ...

EECS 598 - 005 : Theoretical Foundations of Machine Learning Fall 2015 Lecture 15 : Neural Networks Theory

Explain the theoretical and practical model of automatic facade design intelligence in the process of implementing the rules and regulations of facade design and drawing

Artificial intelligence has been trying for decades to create systems with human capabilities, including human-like learning; Therefore, the purpose of this study is to discover how to use this field in the process of learning facade design, specifically learning the rules and standards and national regulations related to the design of facades of residential buildings by machine with a machine ...