[PoliTo] Data Science & Engineering LM - Esame a Scelta

[Flamber]

Buongiorno,

Dopo una triennale in ingegneria elettronica ho deciso di iscrivermi al corso di laurea magistra in Data Science and Engineering che verrà inaugurato quest'anno al politecnico di Torino.
Trovo il corso molto interessante, e mi sembra anche abbastanza sorprendente che ci sia accorti (quantomeno al PoliTo) della necessità di un corso del genere solo nel 2019. Infatti per chi fosse interessato a questo ambito le scelte si limitavano ad un corso in ingegneria matematica o informatica vagamente indirizzati al data processing.

https://didattica.polito.it/pls/portal30/gap.a_mds.vis_coorte?p_sdu=37&p_cds=320&p_lang=EN&p_coorte=2020

Durante il primo semestre c'è un corso a scelta tra tre disponibili e sono abbastanza indeciso.

Di seguito vi posto il programma dei corsi (con i relativi link dove potete vedere i . Personalmente sono molto interessato agli argomenti trattati nel secondo corso "Information Theory and Applications". Tuttavia sono ancora indeciso perchè il mio obiettivo finale è quello di applicare le mie conoscenze al mondo della finanza. Gli altri due esami sono secondo me molto più pertinenti, soprattutto guardando i testi proposti che sono puramente di Ricerca Operativa e Financial Engineering. Inoltre anche i temi trattati nei corsi 1 e 3 sono comunque di mio interesse, anche se tenderei a scartare il 3 dato che molti argomenti simili saranno trattati in un corso del secondo semestre.

Mi farebbe piacere avere uno vostro parere


1) Decision Making and Optimization

1. Linear programming: modeling techniques, basic concepts of the Simplex method and duality (10% of the course).
2. Computational complexity: problem classes P, NP, NP-complete, and CoNP-complete (5% of the course).
3. Exact optimization methods: Branch and Bound, Cutting Planes, and Dynamic Programming (20% of the course).
4. Heuristic optimization methods: greedy algorithms, GRASP, Beam Search, meta-heuristics (Tabu Search, Simulated Annealing, Genetic Algorithms, ACO, VNS, RBS), and math-heuristics (30% of the course).
5. Network flow problems: min cost flow and max flow (5% of the course).
6. Decision making under uncertainty: Stochastic Programming with recourse, Measures for Stochastic Programming, Progressive Hedging method (10% of the course).
7. Nonlinear Programming: theoretical conditions for unconstrained and constrained optimization, algorithms for unconstrained and constrained optimization (20%).

https://didattica.polito.it/pls/portal30/gap.pkg_guide.viewGap?p_cod_ins=01TXCSM&p_a_acc=2020&p_header=S&p_lang=EN


2) Information Theory and Applications
Information Theory

1. Entropy of discrete random variables (15 hours)
o Information source
o Information content and measure
o Entropy and relevant inequalities
o Entropy rate of a source
o Markovian source
o Laboratory:
- Computation of entropies
- Test of entropy inequalities
- Computation of entropy rates

2. Source coding (13 hours)
o Fixed-length encoding
o Fixed-to-variable length encoding
o Source code classification
o Kraft inequality
o McMillan theorem
o Shannon theorem for source codes
o Huffman codes
o Laboratory:
- Efficiency of Huffman codes

3. Discrete channels (12 hours)
o Joint and conditional entropies
o Mutual information
o Entropy and mutual information inequalities
o Laboratory:
- Computation of conditional entropies and mutual information
- Test of relevant inequalities
o Markov chain
o Data-Processing Inequality and its interpretation
o Laboratory:
- Verification of the data-processing inequality
o Shannon theorem and the capacity of symmetric channels
o Laboratory:
- Computation of the capacity of a symmetric channel
- Computation of the capacity of a discrete channel
- Blahut-Arimoto algorithm and its implementation

4. Crypto-Information theory and wiretap channels (10 hours)
o Perfect secrecy
o One-time pad
o Maurer cryptosystem
o Unicity distance
o Wiretap channel
o Effective secrecy capacity
o Laboratory:
- Calculation of the effective secrecy capacity

Applications

6. Algorithms for applied information theory (10 hours)
o Viterbi algorithm
o Belief propagation algorithm
o Decision trees
o Laboratory:
- Basic Viterbi algorithm implementation
- Decision tree algorithm implementation

7. Cryptosystems (10 hours)
o Enciphering, authentication, integrity, attacks
o Private key vs. Public key
o Basic enciphering techniques
o Laboratory:
- Basic enciphering, frequency analysis attack

8. Algorithms for cryptography (10 hours)
o Introduction to RSA
o Notions of AES/DES, Blowfish/Twofish
o Hash functions
o Homomorphic encryption and applications to privacy preserving analytics
o Laboratory:
- Basic example of homomorphic encryption

https://didattica.polito.it/pls/portal30/gap.pkg_guide.viewGap?p_cod_ins=01TXESM&p_a_acc=2020&p_header=S&p_lang=EN


3) Numerical optimization for large scale problems and Stochastic Optimization

Course syllabus

• Convex optimization:
- gradient descent method; conjugate gradient method
- Numerical differentiation
- Newton and quasi-Newton methods
- Globalization techniques
- Alternating direction method of multipliers (ADMM)
• Constrained optimization:
- Interior point methods
- Projected gradient method
- Active set
• Stochastic optimization
- Stochastic Approximation
- Stochastic gradient method

https://didattica.polito.it/pls/portal30/gap.pkg_guide.viewGap?p_cod_ins=01TXDSM&p_a_acc=2020&p_header=S&p_lang=EN