• Introduction to Computer Science

  The course equips the students with a solid basis of computer programming, and exposes them to major themes in computer science such as digital architectures, algorithms, complexity and more. The course uses Java as a first programming language, and emphasizes object-oriented software development.
• Calculus I

  This is the first course in the calculus of one variable intended for Computer Science. Topics covered include the algebraic and analytic properties of the real number system, functions, limits, derivatives and an introduction to integration. Applications including graphing functions, maximising and minimising functions, areas and volumes.
• Linear Algebra I

  Systems of linear equations, Gaussian elimination; Matrices, matrix operations, invertible matrices, a method for inverting matrices; Determinants, Cramer`s rule; Vectors in 2-space and 3-space, dot product, norm of a vector, projections, cross product, lines and planes; Real vector spaces, subspaces, linear independence, basis and dimension, row space, column space and nullspace of a matrix, rank of a matrix
• Discrete Mathematics

  Discrete Math: This is an introductory course in discrete mathematics oriented toward Computer Science. Topics taught in the course include: - fundamental mathematical concepts (definitions, proofs, sets, relations, functions, order relations, proofs by induction), - counting (permutations, combinations, the inclusion-exclusion principle), - basic number theory and graph theory.
• Calculus II

  The second course in the calculus of one variable intended for Computer Science. Topics include integration, infinite sequences and series, power series and Taylor's series, and an overview of multidimensional calculus.
• Linear Algebra II

  Course Syllabus 1. Determinants 2. Linear transformations: kernel, image, composition of transformations, the inverse matrix, the duality of matrices and transformations. 3. Eigenvalues and eigenvectors, the characteristic polynomial, the minimal polynomial, diagonalisation of matrices. 4. Inner products, orthogonality, orthonormality orthogonal matrices
• Logic and Set Theory

  Set theory: naive and axiomatic set theory, cardinals and set comparison, finite and infinite sets, countable sets, cardinality of the continuum, degrees of infinity. Logic: We will study in the course about two logics - propositional logic and first order logic. We will understand the general components of a logic - syntax, semantics, and a proof system - and we will elaborate on each of the components with respect to the two above logics. Among other things, we will study about: Propositional logic - logic connectives, truth tables, logical consequence and equivalence, tautologies and contradictions, normal forms. First-order logic - quantifiers, predicates, functions, structures and models, logical consequence and equivalence, normal forms. As a part of first order logic, we will see examples of algebraic structures, and in particular, we will study basic concepts in group theory.
• Data Structure

  “Good data structures and bad code are much better than the other way around”. Indeed, in order to efficiently and effectively solve challenging computational problems, it is important to organize data in an appropriate form, called data structure. Data structures are an essential area of study and practice for computer scientists and practitioners. The course presents and analyzes classical data structures and related algorithms. In particular, we will learn how to assess the performance of various algorithms for searching, sorting, and manipulating data. We will learn widely-used data structures such as lists, stacks, queues, trees, and hash tables. We will then understand the relations between data structures and the efficiency of algorithms that exploit them. Following is a list of subjects to be covered. Time & space complexity: best case, worst case, average case and amortized – in terms of O, Ω, Θ classes. Data structures: linked list, stack, queue, binary heap, binary search tree, binary search tree with augmented data (e.g., interval tree), suffix trees, B-trees, hash table, KD- trees. Algorithms: in addition to the algorithms related to the data structures, we will also learn comparison based sorting algorithms (heap sort, quick sort, merge sort), linear sorting algorithms (radix sort, counting sort and bucket sort), order statistics algorithms, and in particular the median-of-median algorithm.
• System Programming in C

  The course provides an introduction to procedural programming and work in a UNIX/Linux environment. The course teaches basic topics in the C programming language, such as memory structure, memory management, pointers, and design of procedural programs. In parallel, we will cover fundamental topics in work in a Linux environment, such as managing permissions, working with text files, and programming in shell scripts. The course will involve programming assignments in C in a Linux environment

• Algorithms

  The course teaches techniques for design and analysis of algorithms, and introduces important and widely used algorithms. The course is theoretical in nature, concentrating on the abstract notions involved. Basic graph algorithms such as graph traversals, shortest path, minimum spanning tree, and network flow are covered as well as general techniques such as greedy algorithms and dynamic programming.
• Digital Systems Architecture

  This course reveals the inside of digital computers. The purpose of the course is to explain the principle of operation of the hardware and the relation of the hardware and the software and to give a clear view of how a high-level language program is actually executed by a digital computer. The course starts with Boolean algebra and the implementation of Boolean equations using gates and continues with forming the basic building blocks of a digital computer, e.g., multiplexers, decoders, Arithmetic Logic Unit, registers, counters, state machines etc. At this stage, a simple CPU can be designed. Therefore, a RISC CPU, the MIPS, is defined by introducing its instruction set. The steps of converting a high level program into the MIPS machine language and the mechanism needed to support subroutines are discussed. This is followed by implementation of a basic single cycle MIPS in which the data path and the control part are designed in detail. The next stage is a multi-cycle implementation of the same CPU, including exceptions and interrupts handling. Eventually, a pipelined version is introduced.
• Advanced Programming

  The course will cover advanced programming topics with a focus on practical applications. We will take a deeper look at Java programming, including tasks such as file I/O and text parsing. We will also study broader topics in application programming, such as version control, unit testing and basic GUI development in HTML and Javascript.
• Probability Theory

  Probability theory is a tool of fundamental importance to nearly all the sciences, including Computer Science. This course is intended as an elementary introduction to the mathematical theory of probability. The course covers the following topics: Basic Terms in Probability: Probability Spaces (Sample Space, the Field of Events and the Probability Function), Axioms of Probability, Combinatorial Analysis and Symmetric Sample Space, Conditional Probability and Baye’s Rule, Independency between Events. Random Variables: General Discrete Distributions and Special (Uniform, Geometric, Binomial, HyperGeometric, Poisson, Negative Binomial) and General Continuous Distributions and Special (Uniform, Exponential, Erlang, Normal), Jointly Distributed Discrete Random Variables, Independent Random Variables, Discrete Conditional Distributions, Expectation, Variance, Correlation (CoVariance, Correlation Coefficient), Markov and Chebyshev inequalities, Limit Laws (Central Limit Theorem, Convergence in Probability, the Weak Law of Large Numbers).
• Operating Systems

  This course introduces the logical design of operating systems, emphasizing multi-programmed operating systems and multi-user information systems. The main topics that are discussed in the course are: processes, synchronization, memory management, file systems, I/O devices.
• Machine Learning from Data

  This is an introductory course in machine learning that will present the main principles of data-driven algorithms, supervised and unsupervised learning. Subjects covered include: linear models, Bayesian learning, artificial neural networks, support vector machines, decision trees and more.
• Automata & Formal Languages

  In this course we will study computational models and formal languages, which are important aspects of computer science theory. We will get to know the formal languages: Regular languages and Context free languages. In addition, we will study the basic computational model: automaton (deterministic and non-deterministic), Pushdown Automaton and Formal Grammar.
• Microeconomics

  This course introduces the fundamentals of microeconomics. Theory of demand and supply and equilibrium determination; theories of the firm, production and price determination; discussion of the perfectly competitive market model and of the economics of taxation.
• Finance Fundamentals
• CS Elective Course

  In the second year you need to choose one Computer Science elective course

• Computer Networks

  In recent years, we have witnessed two revolutionary changes in the field of networking. First, the internet has taken over the universe of computer networking. Any serious discussion about computer networking today has to be done with the internet in mind. Second, over the past ten years the biggest growth area has been in networking services and applications, which can be seen in the emergence of the web and ubiquitous use of e-mail services, audio and video streaming, internet phone, and online commerce. This course is organized around a five-layer internet architecture. These five layers consist of the application, transport, network, link, and physical layers.
• Computability & Complexity

  The strive for efficiency is ancient and universal, as time and other resources are always in shortage. Thus, the question of which tasks can be performed efficiently is central to the human experience. In this course we will conduct a systematic exploration of this question through the lens of Computer Science. We will start by studying the theory of Computability, which is concerned with the rigorous definition of computational tasks and the analysis of automated procedures that may solve them. This will set the stage for the theory of Computational Complexity, whose goal is to examine what are the resources that are necessary for any algorithm to solve a given task. Topics covered in the course include Turing machines, universality, non-determinism, the halting problem, recursive and recursively enumerable functions, undecidability, space and time complexity, the classes P and NP, reducibility between decision problems, the Cook-Levin theorem and NP-completeness.
• Computer Graphics

  Fundamental principles of computer graphics including mathematical foundations and algorithms. Methods in image processing, geometric modeling and rendering with Ray tracing and GPU.
• Presentation Skills

  This course equips students with verbal communication skills ahead of their transition from academia to the professional arena. Classes are held in small groups, so to allow all students to practice. The course seeks help the students develop effective verbal communication skills by highlighting the various elements of presentation, such as structure, language, presentation, and content. Students will also be able to improve their verbal skills by using multimedia materials.
• Functional & Logic Programming

  The course will provide an understanding of the theory and implementation of functional programming. The course will review the mathematical basis for functional programming, which is the Lambda calculus, teach the functional programming language Haskell, show how principles of functional programming have penetrated the conventional programming languages and environments, and present models for executing functional programs. The course will also mention the logic programming languages, which are based on First-order logic, and will briefly present the Prolog programming language.
• CS Elective Course

  In the third year you need to choose three Computer Science elective courses.

Below is a list of courses taught in previous years. Course offerings may vary from year to year.

• Securing Information Systems
• 3D Animation with Unreal Engine
• Object Oriented Programming
• Digital Systems Construction
• AI & Morality
• Coding Theory
• Algorithm in Comp. Biology
• Deep Learning
• Statistics & Data Analysis
• Recommendation Systems
• Numerical Optimization with Python
• Unsupervised Learning Methods
• Internet of Things (IoT): Hands On

You may choose elective courses taught in Hebrew

• CS Elective Courses