The courses in the following list are mostly delivered by faculty members affiliated with the interdisciplinary program in applied Math who are potential mentors in the programs.
Notice that the list was freely assembled by faculty affiliated with the program. Each member added courses based on his judgment.
Spring Semester
| Course name + number | Lecturer | Description + Link | Prerequisites |
| Mathematical Epidemiology, 197009 | Nir Gavish, Mathematics | We consider the derivation and analysis of Mathematical models of infectious disease and calibration with data. The course will involve two sections: lectures and a project. | ODE. Some programming experience. |
| Advanced topics in Statistics, 019007 | Barak Fishbain, CEE | The course addresses advanced methods in data and time series analysis. The course is divided into 4 sections: lectures (and tutorials), flipped classroom, theoretical exercise, and programming project.
Topics: Probability Theory Continuous RV, Normal Distribution, Hypotheses Testing, ANoVA Linear regression, Multivariate Regression Sampling Theorem, Monte Carlo Simulations, Damster-Shaffer Theorem Tukey and Bonferroni Methods, Bayesian Shrinkage Priors, Beta Distribution, Distance Sampling Theory, Game Theory, Bayesian Trees and Bayesian Additive Regression Trees, Maximum likelihood, Factor Analysis, Linear Discriminant Analysis, Extreme Value Statistics, Support Vector Machine, Minimum Volume Elispoid Method, Clustering methods, Non-Linear Correlation Measures (e.g., Kandell-Tau) |
Intro to Stats. |
| Optimization under Uncertainty 096335 | Shimrit Shtern
Data and Decision Sciences |
The Course Will Review Modeling and Solution Methods For Optimization Under Uncertainty. The Course Includes The Following Topics# Robust and Distributionally Robust Optimization, Stochastic Optimization,
Chance Constraints, Data-driven Optimization, Solution Methods Including Robust-counterparts and Iterative Methods. Learning Outcomes: 1. Understand The Challenges of Modeling and Solving Optimization Problems With Uncertain Parameters, Including The Limitations of The Various Modeling Techniques and The Computational Challenges Associated With The Various Solution Methods. 2. Formulate Robust Optimization Models For Single- and Multi-stage Optimization Problems With Uncertainty. 3. Solve Different Robust Optimization Models By Using Robust Counterparts and Iterative Methods. 4. Understand How to Incorporate Data in Modeling Uncertainty and The Statistical Meaning of Such Models. 5. Implement The Methods Learned On Real-world Optimization Problems with Uncertainty, Through The Characterization of The Uncertainty And Identification of The Appropriate Modeling and Solution Techniques |
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| Seminar in Analytics of Healthcare Networks 096122 | Noa Zychlinski
Data and Decision Sciences |
The seminar will explore the applications of analytical models in healthcare networks, focusing on how these models support decision-making by integrating medical, operational, and economic factors. Topics will include integrating telemedicine with conventional healthcare, personalized and proactive medicine, and managing mass casualty events. The course will emphasize in-depth analysis and critical evaluation of scientific papers, with each week dedicated to a different paper. | Prior knowledge of stochastic modeling and queueing theory is recommended |
| Seminar in Methodologies in Continuous Optimization 098322 | Nadav Hallak Data and Decision Sciences |
This course focuses on researching problems and models studied in the mathematical continuous optimization literature via a Project-Based Learning approach in which:
We choose a topic and a starting point – a central paper/book studying that topic The participants then explore and analyze the paper: theory, implementation, context to past works, follow-up results Next the participants investigate follow-up research in an effort to form a complete picture on the state-of-the-art: the participants are divided into groups, each explore a prospective follow-up paper. Based on the findings, we continue with exploring the topic from these findings, or summarizing the topic and moving to a new one. |
This is an advanced optimization course
Formal priors are not enforced but prior knowledge should include the nonlinear models course or some background that provides a similar level of understanding of optimization: Multi-dimensional calculus, Algebra, and experience with algorithms. |
| Deep Learning and Approximation Theory | Nadav Dym
Mathematics |
The course will be devoted to approximation theory type questions which are motivated by deep learning. Recall Weirstrass’s theorem which shows that polynomials are dense in the space of continuous functions. From a math perspective, this is the type of results we will often be interested in. The function spaces will be neural networks rather than polynomials. We will show that Neural Networks can also approximate all continuous functions. We will then discuss results which try to explain why neural networks are `better’ than polynomials for approximation. In the second part of the course we will focus on `invariant approximation’, where we aim to approximate continuous functions invariant to some group action via invariant neural networks- function spaces which have this same invariances. | Familiarity with topology in R^d, the notion of uniform convergence of functions, basic familiarity with groups. Recommended for 3rd year math Bsc, or Master students in math/cs/ee/data and decision |
| Autonomous Navigation and Perception 086762 | Vadim Indelman
Aerospace Engineering & Data and Decision Sciences |
The course focuses on fundamental topics in planning under uncertainty in partially observable domains (POMDP, belief space planning) in the context of autonomous navigation and perception, considering online autonomous operation in unknown, uncertain, or dynamically changing environments.
Topics to be covered include: Probabilistic inference, nonparametric inference, MDP and POMDP formulation, belief space planning (BSP), information theoretic costs, search- and sampling-based planning, application to autonomous navigation and active SLAM, Gaussian processes, informative planning and active perception, and online MDP/POMDP solvers. Further details: link |
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| Colloidal Particles and Intermolecular Forces (056396, graduate/undergraduate course) | Ofer Manor
Chemical Engineering |
In this course, we will cover the classic Hamaker theory of van der Waals interactions between particles and the Derjaguin approximation; the equilibrium and electrokinetic theorems of electrical double layers (EDLs), the principle of electrokinetic measurements of EDLs, and EDL force between charged particles in ionic liquids (e.g., water); DLVO theory in Colloid Science and Interaction Energy Diagrams for qualitative stability assessment of colloidal dispersions (suspensions and emulsions); Quantitative stability of colloidal systems: Coagulation kinetics (Shmoluchowski theorem) and contributions from DLVO forces and solvent properties; and optional: Classical Density Functional Theory representation of molecular and colloidal systems for assessing the contribution of depletion and osmotic effects and finite particle and ion size to the interaction between particles. | Undergraduate-level Thermodynamics and ODEs |
| Introduction to computational modeling in Biology (136042 graduate/undergraduate course( | Tom Shemesh
Biology |
This course will review fundamental computational methods employed in modeling biological systems. The emphasis will be placed on “classical” biological modelling problems: enzymatic reactions and diffusion in the microscopic and macroscopic scales, population dynamics, pattern formation…. The students will be introduced to the process of formulating hypotheses in quantitative terms and the focus on a ”hands-on” approach utilizing numerical ODE solvers, stochastic modeling (Gillespie algorithm) and code examples (Matlab, Python or Julia). | |
| Optimization methods in machine learning (00960336 graduate/undergraduate course) |
Dan Garber Data and Decision Sciences |
This course will cover basic algorithmic techniques for solving various continuous optimization problems that underlie many applications in machine learning / statistics and related fields. This is a theory course and the emphasis is on theoretical complexity analysis of efficient methods. | There is no formal prerequisite, but the course is intended for students with strong math and algorithmic capabilities. |
| Transport phenomena – Fluid Flows (058127 graduate course) | Alex Leshansky
Chemical Engineering |
The course covers the fundamentals of low Reynolds number flows. It introduces Cartesian tensors (definitions, properties and operations), reviews differential mass and momentum balances, forces and and stresses, rate-of-strain tensors; equations of fluid flow; boundary conditions (rigid surfaces and free boundary); it further addresses general properties of Stokes (viscous) flows; their fundamental solutions via spherical harmonics and reviews classical problems of viscous flow, such as flow due to a point force, flow past a translating or rotating rigid particle, flow past fluid droplet and gas bubble; the advanced topics, such as Helmholtz minimum rate-of-dissipation principle; the Reciprocal Theorem; Faxén laws, viscous resistance tensors; Boundary Integral Equations and others are reviewed. The frontal teaching is followed by a mini-project on a specific problem/topic in viscous flows (individual projects, in the format of guided reading). | Ordinary and partial differential equations at the level of undergraduate Technion courses; undergraduate fluid mechanics course |
| Deep Learning for Physiological Signals 336209 | Joachim Behar,
Biomedical Engineering |
The task of discovering novel medical knowledge from complex, large-scale and high-dimensional physiological patient data, collected during medical care, is central to innovation in medicine. In this specialization course you will learn about the usage of machine learning within the context of physiological time series analysis. The course will cover the common sources of physiological signals recorded in medical practice, feature engineering (“digital biomarkers”) in the time and frequency domain, entropy measures and deep representation learning approaches. In particular, the usage of CNN and RNN (e.g. LSTM, GRU) for analyzing long continuous streams of physiological data with time dependencies. The necessary theory will be covered. The course assessment will be based on a publication review, presentation and reproduction of a research paper (typically IEEE TBME type) using an open dataset. This is a specialization course and students should come with an engineering background in signal processing and classical machine learning. | Machine learning, signal processing, statistics |
| Computer Aided Geometric Design 236716 | Gershon Elber, Computer Science | Explicit, implicit and parametric representations. Conic Sections and quadratic functions. Hermite and Spline interpolations. Continuity. Differential geometry of curves. Bezier curves. B-spline curves. Differential geometry of surfaces. Bezier and B-spline surfaces. Subdivision and Refinement. The course leads towards the planning and implementation of a graphics system that models 3-dimensional objects using high level programming tools. See also https://class236716.cs.technion.ac.il | |
| Coding for Storage Systems 236520 | Ronny Roth,
Computer Science |
The course will concentrate on the theory and application of coding methods used in common storage devices, such as magnetic disks, optical devices (DVD’s and Blu-ray discs), as well as in bar codes (QR code, PDF417) . A widely-used model for describing the read/write requirements of such storage devices is the so-called “constrained system.” A constrained system is presented by a graph which is similar to a state diagram of a finite-state automaton (or a finite-state machine).
The topics to be covered include the following:
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| Thermodynamics of small systems (1270436) | Saar Rahav
Chemistry |
The course discusses some basic concepts in nonequilibrium statistical mechanics, and how they apply to small scale systems such as molecular machines. Some of the main results in stochastic thermodynamics, such as fluctuation theorems and uncertainty relations, are covered.
The course aims to bring students to a level that allows them to follow current results in the field. |
A basic course in statistical mechanics. |
