97467 - BIG DATA ANALYTICS FOR AUTOMOTIVE MANUFACTURING APPLICATIONS

Scheda insegnamento

Anno Accademico 2021/2022

Contenuti

Algorithms (module 1 – Prof. Mauro Mangia):

  • Introduction to Python: packages for data processing and visualization
  • Data analytics: linear algebra for machine learning, basic of statistics and signal transformation
  • Low level signal processing: digital filtering and power spectral estimation
  • Basics of machine learning: autoregressive models, dimensionality reduction, clustering, classification problem, base of neural networks, autoencoders
  • Streaming algorithms: basic streaming algorithms for feature extraction, streaming approaches for the PCA/PSA problem.

Architectures (module 2 – Prof. Francesco Conti):

  • Recap of basic computer architecture: from high-level languages to Instruction Set Architecture; memory hierarchy; in-order processors. Evaluating computers: latency and throughput, memory bandwidth, energy efficiency metrics.
  • High-performance cache hierarchy: direct mapping & associative caches. Miss rate and penalty. Write-through and write-back caches.
  • High-performance processors: branch prediction, out-of-order execution, speculation (main concepts).
  • Vector and SIMD processing.
  • Multi-cores: shared-memory and distributed parallel computing.
  • Large-scale computing systems: warehouse and streaming computing; brief notes on storage and networking.

Testi/Bibliografia

Main suggested reading for module 1:

Digital Signal Processing: signals, systems and filters
Andreas Antoniou
McGraw-Hill (2006)

Outlier Analysis
Charu C. Aggarwal
Springer (2nd edition, 2017)

Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
MIT Press (2016)

Main suggested readings for module 2:

Computer Architecture: a Quantitative Approach
John L. Hennessy, David A. Patterson
Morgan Kaufmann (2017)

The Datacenter as a Computer: Designing Warehouse-Scale Machines
Luiz A. Barroso, U. Holzle, P. Ranganathan
Morgan & Claypool Publishers (3rd edition)

Basic programming skills (Python / C) are mandatory for the course. Students that have not followed a basic computer architecture course are strongly encouraged to read preliminarily:

Computer Organization and Design RISC-V Edition: The Hardware Software Interface
David A. Patterson, John L. Hennessy
Morgan Kaufmann (2017)

Metodi didattici

Frontal lectures + laboratory exercises with own laptop.

Modalità di verifica e valutazione dell'apprendimento

Learning is assessed by means of a joint oral exam for the two modules on the topics discussed during the frontal lectures.

Strumenti a supporto della didattica

Annotated slides and additional teaching materials available online.

All materials will be shared by means of the official Virtuale site of the course.

Orario di ricevimento

Consulta il sito web di Mauro Mangia

Consulta il sito web di Francesco Conti