Electrical Engineering

Introduction - The Big Data Phenomenon - The Big Data Infrastructure - Datacenter and their relevance to Big Data workloads Datacenter Architecture: Compute - Introduction to GPU, TPU, and other computer architectures Datacenter Architecture: Network - Limitations of TCP for high-performance and big data workloads - RDMA - Datacenter network topologies - Congestion control and routing algorithms - In-network compute: SmartNICs and programmable switches, and use cases involving big data processing Datacenter Architecture: Storage - Brief introduction Big Data Frameworks - Distributed File Systems (HDFS) - MapReduce (Hadoop) - Spark - PySpark + Google Colaboratory Unsupervised Learning: Clustering - Similarity Measures - Algorithms: K-means - Example: Document Clustering Dimensionality Reduction - Feature Extraction - Algorithms: Principal Component Analysis (PCA) - Example: PCA + Handwritten Digit Recognition Supervised Learning - Basics of Machine Learning - Regression/Classification - Algorithms: Linear Regression/Logistic Regression/Random Forest - Examples: - Linear Regression - House Pricing Prediction (i.e., predict the price which a house will be sold) - Logistic Regression/Random Forest - Marketing Campaign Prediction (i.e., predict whether a customer will subscribe a term deposit of a bank) Recommender Systems - Content-based vs. Collaborative filtering - Algorithms: k-NN, Matrix Factorization (MF) - Example: Movie Recommender System (MovieLens) Graph Analysis - Link Analysis - Algorithms: PageRank - Example: Ranking (a sample of) the Google Web Graph Real-time Analytics - Streaming Data Processing - Example: Twitter Hate Speech Detector

The course assumes that students are familiar with the basics of data analysis, machine learning, computer architecture, and computer networks. These must be properly supported by a strong knowledge of foundational concepts of calculus, linear algebra, and probability and statistics. In addition, students must have non-trivial computer programming skills (preferably using Python programming language).

- The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines - RDMA Aware Networks Programming User Manual - Mining of Massive Datasets [Leskovec, Rajaraman, Ullman] (available online) - Big Data Analysis with Python [Marin, Shukla, VK] - Large Scale Machine Learning with Python [Sjardin, Massaron, Boschetti] - Spark: The Definitive Guide [Chambers, Zaharia] - Learning Spark: Lightning-Fast Big Data Analysis [Karau, Konwinski, Wendell, Zaharia] - Hadoop: The Definitive Guide [White] - Python for Data Analysis [Mckinney]

Not mandatory.

Oral examination session, covering a project and/or a scientific paper presentation covering some topics seen during the course. The oral exam includes questions on any subject presented during lectures.

- The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines - RDMA Aware Networks Programming User Manual - Mining of Massive Datasets [Leskovec, Rajaraman, Ullman] (available online) - Big Data Analysis with Python [Marin, Shukla, VK] - Large Scale Machine Learning with Python [Sjardin, Massaron, Boschetti] - Spark: The Definitive Guide [Chambers, Zaharia] - Learning Spark: Lightning-Fast Big Data Analysis [Karau, Konwinski, Wendell, Zaharia] - Hadoop: The Definitive Guide [White] - Python for Data Analysis [Mckinney]

Introduction - The Big Data Phenomenon - The Big Data Infrastructure - Datacenter and their relevance to Big Data workloads Datacenter Architecture: Compute - Introduction to GPU, TPU, and other computer architectures Datacenter Architecture: Network - Limitations of TCP for high-performance and big data workloads - RDMA - Datacenter network topologies - Congestion control and routing algorithms - In-network compute: SmartNICs and programmable switches, and use cases involving big data processing Datacenter Architecture: Storage - Brief introduction Big Data Frameworks - Distributed File Systems (HDFS) - MapReduce (Hadoop) - Spark - PySpark + Google Colaboratory Unsupervised Learning: Clustering - Similarity Measures - Algorithms: K-means - Example: Document Clustering Dimensionality Reduction - Feature Extraction - Algorithms: Principal Component Analysis (PCA) - Example: PCA + Handwritten Digit Recognition Supervised Learning - Basics of Machine Learning - Regression/Classification - Algorithms: Linear Regression/Logistic Regression/Random Forest - Examples: - Linear Regression - House Pricing Prediction (i.e., predict the price which a house will be sold) - Logistic Regression/Random Forest - Marketing Campaign Prediction (i.e., predict whether a customer will subscribe a term deposit of a bank) Recommender Systems - Content-based vs. Collaborative filtering - Algorithms: k-NN, Matrix Factorization (MF) - Example: Movie Recommender System (MovieLens) Graph Analysis - Link Analysis - Algorithms: PageRank - Example: Ranking (a sample of) the Google Web Graph Real-time Analytics - Streaming Data Processing - Example: Twitter Hate Speech Detector

The course assumes that students are familiar with the basics of data analysis, machine learning, computer architecture, and computer networks. These must be properly supported by a strong knowledge of foundational concepts of calculus, linear algebra, and probability and statistics. In addition, students must have non-trivial computer programming skills (preferably using Python programming language).

- The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines - RDMA Aware Networks Programming User Manual - Mining of Massive Datasets [Leskovec, Rajaraman, Ullman] (available online) - Big Data Analysis with Python [Marin, Shukla, VK] - Large Scale Machine Learning with Python [Sjardin, Massaron, Boschetti] - Spark: The Definitive Guide [Chambers, Zaharia] - Learning Spark: Lightning-Fast Big Data Analysis [Karau, Konwinski, Wendell, Zaharia] - Hadoop: The Definitive Guide [White] - Python for Data Analysis [Mckinney]

Not mandatory.

Oral examination session, covering a project and/or a scientific paper presentation covering some topics seen during the course. The oral exam includes questions on any subject presented during lectures.

- The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines - RDMA Aware Networks Programming User Manual - Mining of Massive Datasets [Leskovec, Rajaraman, Ullman] (available online) - Big Data Analysis with Python [Marin, Shukla, VK] - Large Scale Machine Learning with Python [Sjardin, Massaron, Boschetti] - Spark: The Definitive Guide [Chambers, Zaharia] - Learning Spark: Lightning-Fast Big Data Analysis [Karau, Konwinski, Wendell, Zaharia] - Hadoop: The Definitive Guide [White] - Python for Data Analysis [Mckinney]