BSc in Computer Science

• 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
• Computer Science English Advanced 2

  Ms. Noa Kadman

  Students will continue to learn and apply advanced reading skills to enable them to deal with longer and more complex texts. In addition, students will learn to write an academic genre paragraph and present a short presentation to the class.

• Algorithms

  The course covers fundamental ideas in the design and analysis of Algorithms. A thorough knowledge of algorithms allows a computer scientist to determine what problems can be solved using a given amount of resources. The tools and techniques used for the basic algorithms covered in this course can be adapted to provide efficient solutions to novel problems.
• Advanced Programming

  In this course we will cover essential tools that enhance any programmer's toolbox. We will discuss advanced Java topics (that were not covered in the intro class), distributed version control, Python, SQL, HTML, JavaScript and CSS. We will emphasize self-learning, and will touch upon general programming topics such as debugging and unit-tests. The course will give students a solid footing in each topic, that will allow them to keep exploring on their own.
• 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

  An introductory level machine learning course. We will present the main principles of data-driven learning algorithms. We will provide the theoretical basis for learning algorithms and learn about pros and cons of different approaches, from both a mathematical and a practical point of view.

  Students will implement and use algorithms and will practice their application in real life data contexts. Homework and examples will be based on python code developed by the students as well as, in special cases, on using python/sklearn packages.

  Subjects covered include:

  1. 1. Linear models: regression, logistic regression, feature selection
  2. 2. Decision trees , including different approaches to splitting nodes and

  pruning techniques

  3. 3. Bayesean learning, including the advantages and disadvantages of naive vs full Bayes approaches
  4. 4. The perceptron - both primal and dual
  5. 5. kNN
  6. 6. Density estimation and EM
  7. 7. SVMs, including hinge/slack variables and how they affect results
  8. 8. Unsupervised learning - clustering techniques
  9. 9. PAC learning and sample complexity
  10. 10. VC dimension
  11. Statistical estimates of error rates
• Computational Models
• 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 two Computer Science elective courses.

Mandatory Courses The students must choose one of two mandatory courses

• Digital Architectures

   

  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 bui lding 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. We will also show the similarity between the MIPS instruction set and RISC-V instruction set. The RISC-V is nowadays the open source for computer architecture.
• Digital systems

  This course, also known as Nand to Tetris, walks students in the construction of a modern general-purpose computer - hardware and software - from first principles. We will start from the most basic logic gate, known as Nand, and develop from it more gates and chips, leading up to a CPU, memory, and a computer architecture. This will be done using a Hardware Description Language and simulators, like computers are actually developed in industry. We will then develop an assembler, a virtual machine, a compiler for a simple, Java-like programming language, and a basic operating system. We will end this journey by developing a simple computer game like Tetris. It will be a thrill to see this game running on a computer that you built from the ground up. The course provides a deep understanding of many topics in applied computer science, as well as significant system development experience.

• Computer Networks

  This course is organized around the five-layer Internet architecture. These five layers consist of the application, transport, network, link, and physical layers . The course covers fundamental concepts in the design and implementation of computer communication networks, their protocols, and applications. Topics to be covered include: overview of network architectures, applications, network programming interfaces (e.g., sockets), transport, congestion, routing, and data link protocols, addressing, and distributed algorithms/systems. Examples of protocols will be drawn primarily from the Internet (e.g.,HTTP, SMTP, DNS, TCP, UDP, IP, RIP, OSPF, DHCP, ICMP, Ethernet, etc). In addition, we will also cover several Distributed Algorithms. There will be five assignments and a final exam. This course is intended for third year undergraduate computer science.
• Complexity Theory

  This is a mathematical class in which we will study the capabilities and limitations of efficient computation. We will classify various computational problems into different classes according to the amount of resources they require and prove reductions between them. We will then introduce more advanced algorithmic techniques (such as randomized algorithms and approximation algorithms) and study their limitations as well.

  Topics include: time complexity, the classes P and NP, polynomial time reducibility between problems, the Cook-Levin theorem and the theory of NP completeness, self-reducibility and reducibility between search, optimization and decision problems, space complexity, randomized algorithms, approximation algorithms.
• 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 four 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