Data Intelligence Applications

Synthetic program:

1. Pricing in e-commerce
1.1. Introduction to pricing
1.1.1. Scenarios
1.2. Pricing a single product with infinite inventory
1.2.1. Optimization model
1.2.2. Learning the demand curve
1.2.3. Unimodal bandit
1.2.4. Facing non-stationary pricing problems
1.3. Pricing a single product with finite inventory
1.3.1. Optimization model
1.3.2. Algorithms and regret
1.4. Laboratory
1.4.1. Implementing an algorithm to learn the demand curve
1.4.2. Implementing an algorithm to learn unimodal demand curve
1.4.3. Implementing an algorithm to learn a non-stationary demand curve

2. Digital advertising
2.1. Introduction to digital advertising
2.1.1. Funnel and general tools (Analytics, DoubleClick)
2.1.2. Search advertising: players, formats, auctions, available tools (AdWords)
2.1.3. Social advertising: players, formats, auctions, available tools (Facebook)
2.1.4. Display advertising: players, formats, auctions, available tools
2.2. Pay-per-click optimization
2.2.1. Optimization model
2.2.2. Bid-budget optimization algorithms without uncertainty
2.2.3. Learning bid-budget optimization algorithms (combinatorial bandits)
2.2.4. Target segmentation
2.3. Other issues
2.3.1. Funnel based channel interdependency
2.3.2. Publisher-side problems
2.4. Laboratory
2.4.1. Implementing a click-bid curve regression algorithm
2.4.2. Implementing a budget optimization algorithm
2.4.3. Implementing a target segmentation algorithm

3. Social influence
3.1. Introduction to social influence
3.1.1 Markets with network externalities
3.1.2. Small world
3.1.3. From local to global
3.2. Population cascade models
3.2.1. Informational effects
3.2.2. Hard-threshold models
3.2.3. Soft-threshold models
3.2.4. Epidemics
3.3. Influence maximisation algorithms
3.3.1. Maximisation in hard-threshold model
3.3.2. Maximisation in soft-threshold model
3.4. Learning the network
3.4.1. Learning the graph structure (combinatorial bandits)
3.4.2. Regret analysis
3.5. Laboratory
3.5.1. Implementing an algorithm for spreading influence on a network

4. Matching
4.1. Introduction to matching
4.1.1. Scenarios
4.2. Matching problems
4.2.1. Basic matching problems: assignment problem and Hungarian algorithm
4.2.2. Cardinality constraints
4.2.3. Hopcroft-Karp algorithm Edmonds algorithm
4.2.4. 3-dimensional matching
4.3. Stochastic optimization for matching
4.3.1. Kidney exchange
4.4. Learning and matching
4.4.1. Matching while learning
4.5. Laboratory
4.5.1. Implementing some matching algorithms