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Courses

  • Introduction To Computer Science (10016)
    • תקציר הקורס:

    Abstract:

    I?nput and output, arithmetics and logical expressions. conditions, loops, f?unctions, lists, sorting and searching and recursions.
  • Computational Learning (10230)
    • תקציר הקורס:

    Abstract:

    In the framework of the course, a comprehensive foundation will be provided in machine learning techniques. The first part of the course will focus on fundamental learning concepts and supervised learning algorithms. The second part will focus on clustering, and projection to low-dimensional spaces such as Principal Component Analysis (PCA), as well as additional topics like ensemble models, regularization, bootstrap, and an introduction to neural network models.
  • Seminar In Computer Science (11015)
    • תקציר הקורס:

    Abstract:

    The students research an up-to-date advanced algorithm topic and conclude their work by researching and analyzing scientific material, giving a lecture, coding a POC, presenting a demo, and drawing an evaluation plan.

     

    As an advanced student, you have already proved your ability to learn scientific engineering material.

    Thus, in this seminar you are required to independently apply the knowledge and the abilities acquired in the previous stages of your studies.

     

    The overall work (finding and choosing a subject, collecting resource material, writing the proposal,

    completing the POC and its evaluation plan, and presenting in class) requires about 100 hours for completion.

     

    The seminar can be done in pairs, and in special cases even in threes (working in threes requires special approval), when the scope of the work will be accordingly.
  • Final Project in Computer Science (11401)
    • תקציר הקורס:

    Abstract:

    The final project workshop will escort the students in its preliminary stages of the final project selection process and assist to create a comprehensive definition of the broad problem description, prior studies, competitive analysis, goals, objectives & measurements, algorithm design, work plan and code development and evaluation. .
  • Introduction to Artificial Intelligence (19101)
    • תקציר הקורס:

    Abstract:

    This course aims to give the students a basic foundation in Artificial Intelligence (AI) techniques. The first part of the course will focus on fundamental AI concepts of Search & Planning. The second part will include Probabilistic reasoning. The theoretical material will be supported by examples and practical applications.
  • Computational Learning in Medical Systems (50317)
    • תקציר הקורס:

    Abstract:

    This course aims to give the students a basic foundation in machine learning techniques. The first part of the course will focus on fundamental learning concepts and supervised learning algorithms. Covered topics will include methods for solving regression and classification problems, such as: linear and logistic regression, decision trees. The second part will include unsupervised learning methods for clustering and dimensionality reduction (PCA, K-means). The theoretical material will be supported by examples and practical applications from the biomedical world.