MSc Machine Learning & Data Science

Mandatory Core Courses (16 Credits)

• Probability and Statistics for Data Science and Machine Learning

  Prof. Zohar Yakhini

  Students will learn theoretical foundations and practical methodologies for statistical inference, modelling and data analysis. We will start with more advanced probability theory and move to statistical methodology and its practical application in example studies. Methods will be implemented in software (Python, Matlab) and applied in homework projects investigating datasets and practicing the communication of results and conclusions.
• Introduction to Machine Learning

  Prof. Zohar Yakhini

  This is an introductory course in machine learning that will introduce the main principles of data-driven algorithms, including supervised (regression, classification) and unsupervised (clustering, PCA, MLE) learning. Subjects to be covered include: linear models, decision trees, Bayesian learning, parameter estimation and the EM algorithm, PAC learning, artificial neural networks, support vector machines, and more.
• Information Theory

  The rise of Big Data and the vision of a data-driven world present exciting new challenges related to processing Big Data: handling data diversity, exploiting new hardware, software, and cloud-based platforms. These challenges affect all sectors: healthcare, advertising, business intelligence, social sciences, and many more. Platforms will be introduced and used in practice.
• Introduction to Neural Networks and Deep Learning

  Prof. Ya’akov Hel-Or

  This course introduces the basics of neural network concepts and continues to focus on tools for image understanding using Deep Learning. Topics include: the general architectures of Neural Networks (NN), their underlying concepts, optimizing parameters, feed forward and back propagation, CNNs, RNNs, other architectures.
• Advanced Machine Learning

  Dr. Shai Fine

  In this course, we will review the fundamental elements of machine learning – concepts, techniques, and algorithms. Topics to be covered range from data preparation and feature selection, to supervised and unsupervised learning, including advanced methods, such as ensemble learning, support vector machines (SVM), and deep learning. The course will blend statistical inference, information theory, and computer science, thus providing the theoretical intuition behind modern machine learning methods, as well as practical knowledge. The course will include a mix of theoretical and practical exercises, some of which will require programming, as well as a final exam.

Applied Machine Learning and Data Science Elective Courses

• Computer Vision

  Prof. Yael Moses

  In this course, we will learn how to develop an artificial visual system that can interpret visual information from images. A variety of approaches for classic computer vision tasks will be discussed. These include extracting image features, computing the depth from a stereo pair of images, depth from shading, and depth from motion. We will also study higher level tasks, such as image segmentation, object recognition, and the analysis of motion. The course includes theoretical and empirical aspects of these approaches. We will concentrate on classic solutions, but we will also briefly introduce the state-of-the-art approaches that are based on neural networks.
• Natural Language Processing

  Dr. Kfir Bar

  This course teaches the basic foundations of Computational Linguistics and Natural Language Processing (CL/NLP). This domain brings together methods from computer science, linguistics, statistics, cognitive science, and even philosophy, in order to gain insights into how natural language is structured and used. Based on the insights gained, technological applications are developed that can automatically process human language data and extract the main elements of meaning. This course will also demonstrate CL/NLP applications to language technology.
• Image Processing

  Prof. Yacov Hel-Or

  The availability of endless visual data, together with modern hardware for parallel image processing, has transformed image processing and vision fields in the last few years. This course will provide a classic view of image processing and will present the Deep Learning architecture as a successful framework for visual data analysis, with a huge impact in real-world applications.
• Scientific Computing

  The course will familiarize students with the Python scientific stack, including NumPy, SciPy, Matplotlib, and Pandas, and best practices for scientific computing. During each class, we will look at a scientific problem, a method for solving it, and an implementation in Python. Examples will include how to model the spread of infectious diseases, find stationary points for a predator-prey equation system, identify an object in an image, calculate the extinction probability of a rare mutation, analyze results of a tennis game, and plot a map of hurricane density.
• Big Data Platform
• Numerical Analysis

  Dr. Yossi Shamai

  Most scientists and engineers are often faced with tasks that require some knowledge of numerical analysis. Choosing from a wide spectrum of problems, we develop numerical methods in an interactive and friendly way, while analyzing each of the problems. Notions such as consistency, accuracy, and efficiency will be studied.
• Computer Graphics

  Prof. Ariel Shamir

  This course tackles both the representation and presentation issues of data and 3D objects using computers. Visualization and graphics is presented as a method for understanding and exploring data, be it scientific, physical or abstract. Modeling focuses on the different schemes to represent data found in computer-aided mechanical design, medical imaging, games and graphics, and more. The course begins by defining the visualization pipeline and different mapping functions, it presents point-based, boundary, solid and volume representations, scalar and vector field visualization, isosurfaces and volume rendering, and advanced topics such as multi-resolution, mesh compression, shape analysis and feature extraction.
• Topics in Data Mining and Genomics

  Dr. Yaniv Erlich

  The course will introduce definitions, algorithms, statistical methods, and data analysis programming routines relevant for genome biology. The course will consist of lectures, hands-on assignments and student projects. Lecture topics include: databases of genetic and genomic information, sequence (NGS) analysis, phylogenetics, comparative genomics, genotyping methods, association studies, differential expression and more. Hands-on homework assignments will require programming in Python, and will focus on analyzing real data from literature.
• Bioinformatics

  Dr. Ilan Gronau

  The course will cover sequence alignment, probabilistic models, including HMMs, and phylogenetic inference.

Machine Learning and Deep Learning Elective Courses

• Advanced Topics in Deep Learning

  Dr. Kfir Bar

  This course will cover advanced subjects in current deep learning and neural networks techniques, including: Auto-encoders, Generative Adversarial Networks (GAN), Dual-GANs, Transformer networks, Recursive neural networks and LSTMs, Reinforcement learning models, and online learning.
• Unsupervised Learning Methods

  Prof. Ariel Shamir

  The idea of unsupervised learning is to derive insights and understand data without using any known labels. It therefore deals more with finding structures in the data. We will discuss metrics and distance measures, clustering in low and high dimensions, dimensionality reduction, principal component analysis and factorization, LDA, multi-dimensional scaling, T-SNE, and self-organizing maps.
• Reinforcement Learning

  Dr. Moshe Butman

  Reinforcement Learning (RL) is a subfield of Artificial Intelligence that trains a software agent to make autonomous decisions based on its history of rewards. Over the past decade, RL has experienced considerable progress (both research and commercial), especially following the progress in the field of Deep Learning. Over the past few years, many impressive achievements have been reported in RL, including winning games like Chess, Go and in other fields  Autonomous Robots, etc. This course aims to provide a review of the basic concepts and algorithms in the field, particularly in the context of deep reinforcement learning. Python notebooks will be used to demonstrate some of the algorithms.
• Recommendation Systems

  Dr. Asnat Messica

  Recommender systems are widely used by online companies worldwide to provide the most relevant content to their users. They changed the way users find relevant items in various areas such as e-commerce, content websites, social media and more. The goal of this course is to provide the students with both theoretical and practical aspects of recommender systems. We will study machine learning algorithms for personalized recommendations, including recent algorithms, advanced evaluation, research topics, and implementation tools.
• Data Streaming Algorithms and Online Learning

  Dr. Aviv Yehezkel

  Big Data problems more often involve huge amounts of data that cannot even fit in a computer’s storage. This course covers some algorithmic techniques developed for handling such big-data problems. As we shall see, handling such problems forces one to summarize the large amounts of data into compact "sketches" that retain the required important and useful information. The techniques we will study have been successfully applied in wide range of applications, including in databases, computer networks, recommendation platforms, etc. The course will focus on both the theoretical aspects of the design and analysis of such algorithms as well as on the applications.

Big Data and Statistics Elective Courses

• Cloud Computing

  Mr. Dan Amiga

  Cloud computing is a relatively new field, but has already become a standard in development of high computing scalable applications. Utilizing cloud computing allows a single developer access to practically unlimited computing power and storage, therefore enabling the creation of large-scale social networks and technology for finance corporations, search engines, medical research, etc. We will focus on development on top of different cloud infrastructures (AWS/Azure/Google), and will utilize different architectures, tools, and algorithms.
• Advanced Big Data Platforms

  This course is a continuation of Big Data Platforms 1, providing exposure to additional NoSQL tools popular in the industry.
• Application of Machine Learning on Big Data Platforms

  This course will provide a review of Machine Learning algorithms, and their respective python libraries using notebooks. It will expose students to industrial research challenges solved using machine learning and Big Data. As an example, we will see cases of machine learning in the areas of finance, CyberSecurity, sciences, and medical data - all processing data at scale using Hadoop, Spark or Ray.
• Advanced Topics in Statistics and Data Analysis

  Prof. Zohar Yakhini

  We will start with introducing concepts and techniques in stochastic processes: Markov processes, Poisson processes. We will study applications of these concepts in analyzing data from real life domains, including economics, bioinformatics (sequence analysis and NGS) and environmental monitoring data. We will also address topics in information theory (entropy, mutual information, KL divergence, tail algebras of stochastic processes) and survival analysis (log rank test, Cox regression and their applications), as well as multiple testing issues.

Infrastructure and Computer Science General Course Electives

• Distributed Algorithms

  Prof. Gadi Taubenfeld

  The course deals with the design and analysis of distributed algorithms, focusing on issues such algorithms, models (shared memory and message passing), upper and lower bounds, proof methods and impossibility results.
• Cryptography

  Prof. Alon Rosen

  Cryptography is concerned with the design of systems that should be able to withstand any abuse. In this course we will learn how to think precisely about the security of such systems. Specifically, we will learn how to design algorithms that guarantee privacy and authenticity of data during communication and computation, and establish their security using rigorous proofs of security.
• Advanced Cryptography: Secure Computation

  Prof. Elette Boyle

  This advanced seminar will provide students with a formal foundation of cryptographically secure computation protocols, surveying important advances, from its original inception to today, as well as highlighting open directions for future research.
• Network Security

  We will review broadly core areas in computer security. We will start with a general overview of the cyber world: security objectives, understanding of attack types, categories of adversaries and security challenges. We will learn fundamental cryptography and tools, as well as the operational model of the Internet, including its protocols and their vulnerabilities and the essentials of the web network and its inherent dangers. Finally, we will review attack infrastructures and security services. The focus of the course will be on the principles, attack types, and major defense solutions. Time-permitting, we will invite guest industry speakers.
• Advanced Algorithms

  Prof. Tami Tamir

  An advanced course intended mainly for M.Sc. students. The course will address algorithm design and analysis, highlighting several solution techniques. Topics to be covered include approximation algorithms for NP-hard problems, linear programming, randomized algorithms, online algorithms, parameterized complexity, and more.

Preparatory Courses for Non-Computer Science Graduates

• Introduction to CS and Programming in Python

  The course presents key ideas and techniques in computer science and object-oriented programming, using Python. Key elements of the theory and practice of computer science are presented in the context of programming examples and exercises. Through this experience, students develop an appreciation of the elegance and rigor of computer science, and become competent Python programmers.
• Introduction to Operating Systems and Databases

  Prof. Anat Bremler-Bar

  The first half of the course will present basic concepts in operating systems including Processes and threads, Scheduling, Cache, Memory Management, File System, and I/O devices. We will study the Linux OS, and discuss and study cloud computing and virtualization. In the second half, we will present practical and hands-on introduction to SQL and its usage.
• Data Structures and Algorithms for Data Science

  Prof. Shay Mozes

  This class is intended for students in the Data Science M.Sc. program who did not take the classes on data structures and on algorithms in their undergraduate studies. Topics include asymptotic notation (big O, Theta, etc.), worst case analysis (including expected worst case and amortized worst case), data structures - lists, queues, stacks, heaps, binary search trees, and hash tables. Sorting algorithms and lower bound for sorting, graphs and basic algorithms for graphs - depth-first-search, breadth-first-search, shortest paths, minimum spanning trees, the concepts of greedy algorithms, divide-and conquer algorithm, dynamic programming. We will introduce and analyze a few randomized algorithms, and discuss NP completeness and approximation algorithms.
• Intermediate Linear Algebra for Data Science and Machine Learning

  This is a course in linear algebra with special emphasis on topics related to machine learning and data science. It is designed for computer science and data science students. Our course provides a complete coverage of core linear algebra topics. The students may be familiar with some of the topics from their undergraduate studies. The course will revisit and expand topics and will emphasize geometric interpretation and applications.

Mandatory Final Project (5 Credits)

• Final Project

  Independent research and development with industry and/or academic collaboration. Can be extended to a thesis.