Differenze

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--- mds:pa:start [04/06/2017 alle 16:06 (8 anni fa)] – Nicola Ciaramella
+++ mds:pa:start [08/09/2021 alle 12:25 (4 anni fa)] (versione attuale) – [Instructor] Nicola Ciaramella
@@ Linea 1: / Linea 1: @@
-====== Programmatic Advertising A.Y. 2016/17 ======
+====== Programmatic Advertising A.Y. 2019/20 ======
 When you submit Google (or another search engine) a request, you fire an online auction: in much less than a second, many advertisers’ algorithms launch their offers and a Google algorithm chooses the winners, which gain the right to show you their advertisements. Similar events happen when you enter a web site, a social network or an app delivering you advertisements.\\
@@ Linea 5: / Linea 5: @@
 We will explore how these algorithms work, how a search engine or a newspaper maximize their profit, how an advertiser targets its potential customers. The goal is to provide the student with the background required to join a real-life project team in a media agency and design a strategy for optimal management of an advertising campaign. The same framework is also a basis for e-commerce campaigns.\\
 The description is high-level, focusing the conceptual framework instead of the programming task. Indeed, often one can algorithmically manage advertising and e-commerce campaigns without explicit programming, using existing algorithmic platforms. Nonetheless, the mindset required is still analytical and algorithms-oriented. Our ambition is to help the student to develop such a mindset, pertaining this specific field.\\
-No advanced mathematics or computer science background is required.\\
+No advanced background in mathematics or computer science is required.
-Contents:\\
-•	Digital advertising and e-commerce: business context; programmatic vs. traditional approach; the profession of programmatic advertising; the economy of online auctions.\\
-•	Decision-making: expected utility, optimization, heuristics.\\
+=====Contents=====
-•	Multi-armed bandits: a conceptual framework for maximizing profit in a dynamic uncertain environment.\\
-•	Basic statistical tools: distributions (like Normal, Binomial, Beta); concept of simulation; Bayesian inference.\\
+  * Digital advertising and e-commerce: business context; programmatic vs. traditional approach; the profession of programmatic advertising; the economy of online auctions.
-•	Forecasting: regression; time series analysis; matrix-based methods.\\
+  * Decision-making: expected utility, optimization, heuristics.
-•	Advertising and e-commerce with customer profiles available.
+  * Multi-armed bandits: a conceptual framework for maximizing profit in a dynamic uncertain environment.
+  * Basic statistical tools: distributions (like Normal, Binomial, Beta); concept of simulation; Bayesian inference.
+  * Forecasting: regression; time series analysis; matrix-based methods.
+  * Advertising and e-commerce with customer profiles available.
+=====Instructor=====
+  * **Nicola Ciaramella**
+    * [[[email protected]]]
+=====Teaching Material=====
+  * {{ :mds:pa:2.1_single_resource_capacity_allocation_-_part_1.pdf |2.1_single_resource_capacity_allocation_-_part_1.pdf}}
+  * {{ :mds:pa:2.2_single_resource_capacity_allocation_-_part_2.pdf |2.2_single_resource_capacity_allocation_-_part_2.pdf}}
+  * {{ :mds:pa:3.3._logistic_regression_example_phone_service_.pdf |3.3._logistic_regression_example_phone_service_.pdf}}
+  * {{ :mds:pa:bayes_in_medicine.pdf |bayes_in_medicine.pdf}}
+  * {{ :mds:pa:bayes_rule_slides_.pdf |bayes_rule_slides_.pdf}}
+  * {{ :mds:pa:decision_theory_-_airplane_example.ppt |decision_theory_-_airplane_example.ppt}}
+  * {{ :mds:pa:decision_theory_-_used_car_example.pdf |decision_theory_-_used_car_example.pdf}}
+  * {{ :mds:pa:dynamic_pricing_-_part_1.pdf |dynamic_pricing_-_part_1.pdf}}
+  * {{ :mds:pa:dynamic_pricing_-_part_2.pdf |dynamic_pricing_-_part_2.pdf}}
+  * {{ :mds:pa:logistic_regression_-_part_1.pdf |logistic_regression_-_part_1.pdf}}
+  * {{ :mds:pa:logistic_regression_-_part_2.pdf |logistic_regression_-_part_2.pdf}}
+  * {{ :mds:pa:multi_armed_bandits_-_part_1.pdf |multi_armed_bandits_-_part_1.pdf}}
+  * {{ :mds:pa:multi_armed_bandits_-_part_2.pdf |multi_armed_bandits_-_part_2.pd}}
+  * {{ :mds:pa:multi_armed_bandits_-_part_3.pdf |multi_armed_bandits_-_part_3.pdf}}
+  * {{ :mds:pa:multiarmed_bandits_word_-_part_1.pdf |multiarmed_bandits_word_-_part_1.pdf}}
+  * {{ :mds:pa:naive_bayes_ctr_prediction.pdf |naive_bayes_ctr_prediction.pdf}}
+  * {{ :mds:pa:naive_bayesian_prediction_-_example.pdf |naive_bayesian_prediction_-_example.pdf}}
+  * {{ :mds:pa:probability_distributions_-_part_1.pdf |probability_distributions_-_part_1.pdf}}
+  * {{ :mds:pa:probability_distributions_-_part_2.pdf |probability_distributions_-_part_2.pdf}}
+  * {{ :mds:pa:probability_distributions_-_part_3.pdf |robability_distributions_-_part_3.pdf}}
+  * {{ :mds:pa:probability_distributions_with_excel_-_example.xlsx |probability_distributions_with_excel_-_example.xlsx}}
+  * {{ :mds:pa:regression.pdf |regression.pdf}}
+  * {{ :mds:pa:trendlines.pdf |trendlines.pdf}}
+  * {{ :mds:pa:trendlines_examples.xlsx |trendlines_examples.xlsx}}