Vector and matrix operations. Vector and matrix norms. SVD. Matrix Factorization. Exploiting matrix structure. Iterative methods.
Syllabus
Optimization
Convex optimization. Duality. First and second order methods. Interior points. Stochastic optimization.
Bayesian Statistic and Probabilistic Programming
Computational statistics. Bayesian statistics. Computational sampling techniques. Probabilistic programming.
Machine Learning
Statistical learning theory. Supervised learning. Generative models. Linear models. Kernel methods. Ensemble methods. Manifold learning. Clustering.
Agile Data Science
Agile development for data science projects. Data infrastructures. Solution deployment. Privacy and ethics.
Presentation and Data Visualization
Perception and patterns theory. Data and visualization models. Creation of visualizations.
Big data *
Large scale processing frameworks. Hadoop. Spark.
Advanced databases techniques *
Real time and batch data ingestion. SQL and large scale database design. NoSQL solutions.
Deep learning *
Introduction to tensorflow. Fully connected networks. Convolutional neural networks. Recurrent neural networks. Unsupervised deep learning.
Recommenders *
Collaborative filtering. Item based recommenders. Hybrid models. Context-aware recommenders. Large scale recommenders.
Probabilistic graphical models *
Bayesian networks. Markov networks. Exact and approximate inference. PGM decision making.
Business Analytics *
Natural Language Processing *
Introduction to natural languge processing. Syntax and parsing, Text Similarity, Sentiment Analysis and Semantics, Text Summarization
Computer Vision *
Introduction to images. Image processing. Retrieving information from images.
Complex Networks *
Models of complex networks. Micro and macro scale. The mesoscale. Network Dynamics.
(* Eligible Course)
In order to successfully finish the master’s program, students are required to complete, durng the second semester, a capstone project. Capstone projects are “experiential” projects where students take what they’ve learned throughout the master program and apply it to examine a specific idea.