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Table of contents
Part 1: Classical approaches to scalability
Part 2: Scalability in statistical learning
Part 3: Scalability in modern machine learning
@@ -68,7 +69,7 @@ Literature, resources
These slides
Complementary (rapidly evolving) lecture notes
- Tim Roughgarden: Algorithms Illuminated
+ Tim Roughgarden: Algorithms Illuminated
Papers including these that we will use:
- Siegelmann, On the computational power of neural nets, 1992
@@ -326,7 +327,7 @@
Trick 2: Multi-tape Turing machines
- Lecture 4: Recursive functions, halting problem, Kolmogorov complexity
+ Lecture 4: Recursive functions, halting problem
@@ -354,8 +355,23 @@ Halting problem
+
+ Lecture 5: Kolmogorov Complexity
+
+
+
+ Definition
+ Kolmogorov complexity $K(x)$ of string $x$ is $$\min_{\{p ~|~ U(p)=x\}} l(p)$$ where $U$ is a universal Turing machine and $l(p)$ is the length of string $p$.
+
+
- Kolmogorov Complexity
+ Theorem
+ The following problems cannot be solved by an algorithm:
+
+ - Kolmogorov complexity
+
- Algorithm equivalence
+
- Algorithm triviality
+