magistraleinformatica:ad:ad_22:start
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Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente | ||
magistraleinformatica:ad:ad_22:start [22/05/2023 alle 17:38 (2 anni fa)] – Roberto Grossi | magistraleinformatica:ad:ad_22:start [03/07/2023 alle 12:07 (24 mesi fa)] (versione attuale) – Roberto Grossi | ||
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^ Date ^ Topics ^ References and notes ^ | ^ Date ^ Topics ^ References and notes ^ | ||
- | |22.02.2023 | Introduction to the class. | | | + | |22.02.2023 | Introduction to the class. Course organization, |
|24.02.2023| Playing with probability. Random indicator variables: secretary problem and random permuting (suggested reading: birthday paradox). Randomized quick sort. | [CLRS 5.1-5.3 (optional 5.4.1), {{: | |24.02.2023| Playing with probability. Random indicator variables: secretary problem and random permuting (suggested reading: birthday paradox). Randomized quick sort. | [CLRS 5.1-5.3 (optional 5.4.1), {{: | ||
|27.02.2023| Virus scan and stream analysis with Karp-Rabin fingerprints: | |27.02.2023| Virus scan and stream analysis with Karp-Rabin fingerprints: | ||
|01.03.2023| Universal hashing. Markov' | |01.03.2023| Universal hashing. Markov' | ||
|03.03.2023 | Weekly hands-on. | see Teams drive | | |03.03.2023 | Weekly hands-on. | see Teams drive | | ||
- | |06.03.2023 | Introduction to game theory. The theory of rational choice. Strategic games. The prisoner dilemma. Pollution game. Bach or Stravinsky. Matching Pennies. Stag hunt.| see Teams drive | | + | |06.03.2023 | Introduction to game theory. The theory of rational choice. Strategic games. The prisoner dilemma. Pollution game. Bach or Stravinsky. Matching Pennies. Stag hunt. (F. Geraci) |
- | |08.03.2023 | Nash equilibrium. Review of example strategic games. Nash equilibrium of stag hunt with n players. Best response. Using best response to find the Nash equilibrium. | see Teams drive | | + | |08.03.2023 | Nash equilibrium. Review of example strategic games. Nash equilibrium of stag hunt with n players. Best response. Using best response to find the Nash equilibrium. |
- | |10.03.2023 | Weekly hands-on. | see Teams drive | | + | |10.03.2023 | Weekly hands-on. |
- | |13.03.2023 | Game Theory. Improving and best response. Dominated actions. Vickrey auction (aka second price auction). Expected payoffs. | see Teams drive | | + | |13.03.2023 | Game Theory. Improving and best response. Dominated actions. Vickrey auction (aka second price auction). Expected payoffs. |
- | |15.03.2023 | Mixed strategy Nash equilibrium. Example: matching penny. Example: kicking penalty. Stable matching. | see Teams drive | | + | |15.03.2023 | Mixed strategy Nash equilibrium. Example: matching penny. Example: kicking penalty. Stable matching. |
- | |17.03.2023 | Weekly hands-on. | see Teams drive | | + | |17.03.2023 | Weekly hands-on. |
|20.03.2023| Worst-case constant-time lookup: Cuckoo hashing. | {{: | |20.03.2023| Worst-case constant-time lookup: Cuckoo hashing. | {{: | ||
|22.03.2023| Proxy caches and dictionaries with errors: Bloom filters. | {{: | |22.03.2023| Proxy caches and dictionaries with errors: Bloom filters. | {{: | ||
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|03.04.2023| Document resemblance with MinHash, k-sketches and the Jaccard similarity index. Azuma-Hoeffding bound. Triangle counting. | [[http:// | |03.04.2023| Document resemblance with MinHash, k-sketches and the Jaccard similarity index. Azuma-Hoeffding bound. Triangle counting. | [[http:// | ||
|05.04.2023| Count-Min sketches for frequent elements.| {{http:// | |05.04.2023| Count-Min sketches for frequent elements.| {{http:// | ||
- | |08.05.2023| | + | |14.04.2023 | Weekly hands-on. | see Teams drive | |
- | |10.05.2023| | + | |17.04.2023 | The data stream model. Cardinality estimation. Linear counting. LogLog counters. (F. Geraci) | see Teams drive | |
+ | |19.04.2023 | Bloom filters (probabilistic deletion | ||
+ | |21.04.2023 | Weekly hands-on. (F. Geraci) | see Teams drive | | ||
|24.04.2023| Networked data and randomized min-cut algorithm for graphs. | {{: | |24.04.2023| Networked data and randomized min-cut algorithm for graphs. | {{: | ||
|26.04.2023| Approximation in fine-grained algorithms and limitations. Case study: diameter in undirected unweighted graphs. | {{ : | |26.04.2023| Approximation in fine-grained algorithms and limitations. Case study: diameter in undirected unweighted graphs. | {{ : | ||
|03.05.2023| Fine-grained algorithms. SETH conjecture and conditional lower bounds. Guaranteed heuristics. Case study: diameter in undirected unweighted graphs. | [[https:// | |03.05.2023| Fine-grained algorithms. SETH conjecture and conditional lower bounds. Guaranteed heuristics. Case study: diameter in undirected unweighted graphs. | [[https:// | ||
+ | |08.05.2023| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs. | ||
+ | |10.05.2023| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https:// | ||
|12.05.2023| Local search, Greedy, Randomized: case study of max cut for graphs. | {{: | |12.05.2023| Local search, Greedy, Randomized: case study of max cut for graphs. | {{: | ||
|15.05.2023| NP-hard problems: download file manager and the knapsack problem. Dynamic programming algorithms for Knapsack: Case 1: integer weights, complexity O(nW). Case 2: integer values, complexity O(n< | |15.05.2023| NP-hard problems: download file manager and the knapsack problem. Dynamic programming algorithms for Knapsack: Case 1: integer weights, complexity O(nW). Case 2: integer values, complexity O(n< | ||
|17.05.2023| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ : | |17.05.2023| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ : | ||
magistraleinformatica/ad/ad_22/start.1684777134.txt.gz · Ultima modifica: 22/05/2023 alle 17:38 (2 anni fa) da Roberto Grossi