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Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- magistraleinformatica:ad:ad_22:start [24/02/2023 alle 11:33 (2 anni fa)] – Roberto Grossi
+++ magistraleinformatica:ad:ad_22:start [03/07/2023 alle 12:07 (24 mesi fa)] (versione attuale) – Roberto Grossi
@@ Linea 9: / Linea 9: @@
   * The course will start on Feb. 22, 2023.
-==== Schedule ====
+==== Schedule (work in progress) ====
   * Class hours: Mon 09‑11 (Fib-X1), Wed 09‑11 (Fib-L1), Fri 11-13 (Fib-L1)
@@ Linea 47: / Linea 47: @@
-| **Date** | **Day** | **Type** | **From** | **To** | **h** | **Topics** | Notes |
+^ Date ^ Topics ^ References and notes ^
-| 02/22/2023 | Wed | lecture | 9.00 | 11.00 | 1 | Introduction to the class. | |
+|22.02.2023 | Introduction to the class. Course organization, schedule, and purpose. | |
-| 02/24/2023 | Fri | lecture | 11.00 | 12.00 | 1 | Randomization and indicator variables |  |
+|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), {{:magistraleinformatica:alg2:algo2_13:randqs.pdf|par. 7.3}}] [[https://repl.it/L9KD/1|code]] |
+|27.02.2023| Virus scan and stream analysis with Karp-Rabin fingerprints: randomized checking and pattern matching. Montecarlo and Las Vegas algorithms. | [RM {{:magistraleinformatica:alg2:algo2_15:karp-rabin-1.pdf| par.7.4-7.6}}]  [[https://repl.it/LbWC/3|code]]|
+|01.03.2023| Universal hashing. Markov's inequality. Perfect hashing.| [CLRS 11.2, 11.3.3, CLRS 11.5 ] [[https://repl.it/Ljuh/8|code]]|
+|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. (F. Geraci) | 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. (F. Geraci) | see Teams drive |
+|10.03.2023 | Weekly hands-on. (F. Geraci) | see Teams drive |
+|13.03.2023 | Game Theory. Improving and best response. Dominated actions. Vickrey auction (aka second price auction). Expected payoffs. (F. Geraci) | see Teams drive |
+|15.03.2023 | Mixed strategy Nash equilibrium. Example: matching penny. Example: kicking penalty. Stable matching. (F. Geraci) | see Teams drive |
+|17.03.2023 | Weekly hands-on. (F. Geraci) | see Teams drive |
+|20.03.2023| Worst-case constant-time lookup: Cuckoo hashing. | {{:magistraleinformatica:alg2:algo2_10:cuckoo-undergrad.pdf|Notes}} {{:magistraleinformatica:alg2:algo2_16:cuckoohashinsertion.pdf|Notes}}  [[https://repl.it/NzGN/3|code]]|
+|22.03.2023| Proxy caches and dictionaries with errors: Bloom filters. | {{:magistraleinformatica:alg2:bloomfiltersurvey.pdf|Survey: except par.2.5-2.6 (optional: par.2.2)}} |
+|24.03.2023 | Weekly hands-on. | see Teams drive |
+|27.03.2023| Space-efficient storage of sets with approximate memberships: upper and lower bounds. | {{ :magistraleinformatica:ad:ad_19:bloomcuckoorank.pdf | Notes (second part)}} |
+|29.03.2023| Distributed server and load balancing through hashing. | [[https://jeremykun.com/2015/12/28/load-balancing-and-the-power-of-hashing/|blog]] [[https://www.cs.cmu.edu/afs/cs/project/pscico-guyb/realworld/www/slidesF15/hashing.pdf|Sect.7 and 8.1]] |
+|31.03.2023 | Weekly hands-on. | see Teams drive |
+|03.04.2023| Document resemblance with MinHash, k-sketches and the Jaccard similarity index. Azuma-Hoeffding bound. Triangle counting. | [[http://gatekeeper.dec.com/ftp/pub/dec/SRC/publications/broder/positano-final-wpnums.pdf|paper]] [[http://cs.brown.edu/courses/cs253/papers/nearduplicate.pdf|paper]] [[http://homes.cs.washington.edu/~jrl/cs525/scribes08/lec10.pdf|Azuma-Hoeffding]] [[https://repl.it/MDNO/3|code]]|
+|05.04.2023| Count-Min sketches for frequent elements.| {{http://dimacs.rutgers.edu/~graham/pubs/papers/cm-full.pdf| sects.1-3, 4.1}} [[https://sites.google.com/site/countminsketch/|Site]] {{:magistraleinformatica:alg2:algo2_12:count-min-sketch.pdf|Notes}} [[https://repl.it/Lvob/3|code]]|
+|14.04.2023 | Weekly hands-on. | see Teams drive |
+|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 and counting). Count min sketch. Heavy hitters. The space saving algorithm. (F. Geraci) | see Teams drive |
+|21.04.2023 | Weekly hands-on. (F. Geraci) | see Teams drive |
+|24.04.2023| Networked data and randomized min-cut algorithm for graphs. | {{:magistraleinformatica:alg2:algo2_15:mincut1.pdf| par.1.1}} |
+|26.04.2023| Approximation in fine-grained algorithms and limitations. Case study: diameter in undirected unweighted graphs. | {{ :magistraleinformatica:ad:ad_17:diameterapprox.pdf | notes }} |
+|03.05.2023| Fine-grained algorithms. SETH conjecture and conditional lower bounds. Guaranteed heuristics. Case study: diameter in undirected unweighted graphs. | [[https://www.dropbox.com/s/zq0dklabkjyd302/20171212.pdf?dl=0|notes]] [[https://people.csail.mit.edu/virgi/ipec-survey.pdf|sect. 2.3, 2.4, 3, 4]]|
+|08.05.2023| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs.  | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 2.2.1, 3.1]] |
+|10.05.2023| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 5.2, 5.3]] |
+|12.05.2023| Local search, Greedy, Randomized: case study of max cut for graphs. | {{:magistraleinformatica:alg2:algo2_14:lec02.pdf|Notes}} |
+|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<sup>2</sup>vmax). Examples.  General inapproximability results. Case study: travel salesman problem (TSP).  2-approximation algorithms for metric TSP. | [CLRS 35.2] [[https://math.mit.edu/~goemans/18434S06/knapsack-katherine.pdf|notes]] |
+|17.05.2023| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ :magistraleinformatica:ad:ad_17:notesknapsack2.pdf |notes}} [[https://math.mit.edu/~goemans/18434S06/knapsack-katherine.pdf|notes]] [[https://repl.it/@grossiroberto/ApproxKnapsack|code]] |