The Data Science Institute at Bar-Ilan University
open research assistant position
המשרה אוישה!
Brain Dynamic Lab – open positions
The Human Brain Dynamic Lab
Dr. Elana Zion Golumbic
דרושים סטודנטים מצטיינים בעלי אורייטנציה חישובית חזקה
לפרוייקט חדש המשלב מדעי המוח-הנדסה
של פיתוח ממשק מוח-מכונה לשיפור יכולות מיקוד קשב
תיאור הפרוייקט: הפרוייקט הינו שיתוף פעולה בין חוקרים ממדעי המוח (דר׳ אילנה ציון גולומביק) והנדסה (פרופ׳ שרון גנות ופרופ׳ יעקב גולדברגר), שמטרתו פיתוח עזר שמיעה חכם, המבוסס ממשק מוח-מכונה, בעל יכולות מיקוד קשב לסיוע בהקשבה בסביבות רועשות. הפרוייקט יעשה שימוש ברישום גלי מוח (EEG) ותנועות עיניים, אלגוריתמים מתקדמים להפרדת אותות ולמידת עומק.
דרישות: סטודנטים מצטיינים לתואר שני או דוקטורט במדעי המוח, הנדסה או מדעי המחשב.
פתוח גם לסטודנטים מצטיינים בשנה האחרונה של התואר הראשון במדעי המוח/הנדסה הרואים את עתידם במחקר.
למעוניינים, נא לשלוח קורות חיים ופסקה קצרה על הנסיון המחקרי שלכם ל:
דר׳ אילנה ציון גולומביק elana.zion-golumbic@biu.ac.il
פרופ׳ שרון גנות: Sharon.Gannot@biu.ac.il
פרופ׳ יעקב גולדברגר: Jacob.Goldberger@biu.ac.il
משרת סטודנט
לקליניקה של המחלקה לפסיכולוגיה דרוש/ה
במעבדה לחקר פסיכותרפיה מתקיים פרויקט ייחודי בחזית המחקר עובד/ת לסיוע בניהול ותפעול מערכתחכמה לאיסוף נתונים.
במסגרת פרויקט זה נערך מעקב מחקרי שוטף אחר טיפולים המתבצעים בקליניקה לשירות הקהילה במטרה לקדם את האפקטיביות של הטיפולים. המחקר מנוהל על ידי מערכת שתוכנתה במיוחד לצרכי הקליניקה והמחקר בה שנמצאת כיום בשלבי פיתוח מתקדמים.
דרוש/ה סטודנט בעל יכולות טכניות ותכנותיות גבוהות ולמידה עצמאית.
התפקיד כולל עבודה צמודה עם מפתחי התכנה לשם היכרות עם המערכת ובמטרה לסייע בפיתוח ובפתרון תקלות.
דרישות התפקיד:
חובה – OFFICE
חובה – היכרות עם מערכות מבוססות Linux
חובה – הבנה טכנית טובה של חומרת מחשב ותכנה
רשות – הכרה של תוכנות סטטיסטיקה
רשות – יכולות בתכנות (מטלב, C)
רשות – הבנה באיסוף ופיענוח אותות פיזיולוגיים
נדרשת זמינות גבוהה
תנאים טובים למתאימים
להגשת מועמדות ולפרטים נוספים יש לשלוח קורות חיים למייל: biuresearchclinic@gmail.com
משרת סטודנט לעבודה לפי שעות
המשרה אוישה!
לפרוייקט המחקר “על מה מדינות מדברות?” (במימון ISF – הקרן הלאומית למדע) שעוסק בניתוח טקסטואלי ממוחשב דרוש/ה סטודנט/ית לעבודה לפי שעות (היקף של רבע-שליש משרה לאורך השנה האקדמית). עיקר העבודה הינה ללוות בהיבט תכנוני את הפרויקט על חלקיו שונים ולסייע בכתיבת הקוד לניתוח הטקסטים בהתבסס על גישות שונות לניתוח טקסטואלי ממוחשב.
דרש ידע בתכנות R או פייטון ורקע בניתוח ועיבוד טקסטואלי/גישות NLP.
תחילת עבודה – 1.11.2019 . להגשת מועמדות ופרטים נוספים, אנא פנו לד”ר מור מיטרני mor.mitrani@biu.ac.il
Data Science Courses, Bar-Ilan University
This table includes courses given at Bar-Ilan that may be relevant to students interested in Data Science.
Note: full and accurate course details should be taken from Bar-Ilan official course catalog. We’ll do our best to keep this table updated.
(legend – see below table)
| Course # | Course Name | Department | Category | Target Audience | Points | Type | Semester | Degree | Given in TASHAPB |
|---|---|---|---|---|---|---|---|---|---|
| 13619 | Digital Humanities and the Analysis of Hebrew Texts | Jewish Literature | NLP/Text | Exposure | 2 | Lecture | Annual | Both | y |
| 272000 | Introduction to Programming using python | Brain Science | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 27208 | Introduction to Probability | Brain Science | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
| 27213 | Introduction to Statistics | Brain Science | Statistics/Probablity | Core | 1.5 | Lecture | B | Undergrad | y |
| 27305 | Signal Processing | Brain Science | Signal Processing | Core | 1 | Lecture | B | Undergrad | y |
| 27436 | Neuronal Network | Brain Science | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 27437 | Information Theory and Learning Methods | Brain Science | ML/DL/NN | Core | 1.5 | Lecture | B | Undergrad | y |
| 275020 | Data Science Applications in Neuroscience | Brain Science | ML/DL/NN | Applied | 1 | Workshop | A | Grad | y |
| 275021 | Data Science Applications in Neuroscience | Brain Science | ML/DL/NN | Applied | 1 | Workshop | A | Grad | y |
| 275027 | Introduction to Programming with Python | Brain Science | Programming | Applied | 1 | Lecture | A | Grad | y |
| 27504 | Theories on Nerve Networks and Machine Learning | Brain Science | ML/DL/NN | Core | 2 | Lecture | A | Undergrad | |
| 27505 | Signal and Data Analysis in Neuroscience | Brain Science | Signal Processing | Core | 2 | Lecture | B | Grad | y |
| 278237 | Artificial intelligence: from humanoids to swarms of thinking machines | STS | AI | Exposure | 2 | Lecture | Annual | Undergrad | y |
| 35599 | Data Science practicum | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
| 35603 | Algorithms I | Information Science | Algorithms/Data Structures | Applied | 2 | Lecture | A | Undergrad | y |
| 35605 | Big Data | Information Science | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
| 35615 | Programming Basics | Information Science | Programming | Exposure | 1 | Lecture | B | Undergrad | y |
| 35616 | Advanced programming | Information Science | Programming | Applied | 1 | Lecture | B | Undergrad | y |
| 35617 | Advanced data analysis | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
| 35625 | Big Data applications | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 35626 | Introduction to Data Science | Information Science | ML/DL/NN | Applied | 1.5 | Lecture | A | Both | |
| 35633 | Data Science introduction | Information Science | ML/DL/NN | Applied | 1 | Lecture | B | Both | y |
| 35712 | Digital Humanities | Information Science | Digital Humanities | Applied | 1 | Lecture | A | Both | y |
| 35728 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
| 35733 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 35809 | Geographic Information Systems | Information Science | GIS | Applied | 1 | Lecture | B | Grad | y |
| 35810 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Grad | |
| 35819 | Medical Informatics | Information Science | Medical | Applied | 1 | Lecture | B | Grad | y |
| 35858 | Data Visualization | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
| 35867 | Introduction to Programming - Python | Information Science | Programming | Applied | 1 | Lecture | B | Grad | y |
| 35869 | Advanced Python | Information Science | Programming | Applied | 1 | Lecture | A | Grad | y |
| 35879 | The Semantic Web | Information Science | Digital Humanities | Applied | 1 | Lecture | A | Grad | y |
| 35880 | Algorithms 2 | Information Science | Algorithms/Data Structures | Applied | 1 | Lecture | A | Grad | |
| 35887 | Machine Learning | Information Science | ML/DL/NN | Applied | 1 | Lecture | B | Grad | y |
| 35890 | Introduction to digitization of textual and graphic information | Information Science | Digital Humanities | Applied | 1 | Lecture | B | Grad | y |
| 35954 | Selected issues in Digital humanities | Information Science | Digital Humanities | Exposure | 1 | Seminar | B | Grad | y |
| 35955 | Semantic web applications for digital humanities | Information Science | Digital Humanities | Applied | 1 | Seminar | B | Grad | y |
| 55002 | Intro to Statistics I | Management | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
| 55003 | Intro to Statistics II | Management | Statistics/Probablity | Applied | 1 | Lecture | B | Undergrad | y |
| 55006 | Introduction to Probability | Management | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
| 55089 | Service Management | Management | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 55505 | Logistic Information System Management | Management | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
| 55703 | Information System Management in Industry | Management | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 60066 | Intro to R | Psychology | Programming | Applied | 1 | Lecture | A | Grad | y |
| 60067 | Multilevel Modeling and Dyadic Analysis | Psychology | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
| 66153 | Introduction to Statistics I | Economics | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
| 66154 | Introduction to Statistics II | Economics | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
| 66862 | Python for Economists Introductory | Economics | Programming | Applied | 0.5 | Lecture | A | Undergrad | y |
| 66863 | Economics | Programming | Applied | 0.5 | Lecture | B | Undergrad | y | |
| 66880 | Econometrics of Time Series | Economics | Finance/Econometrics | Applied | 1 | Lecture | B | Undergrad | |
| 70647 | Text Mining | Buisness Administration | NLP/Text | Applied | 1 | Lecture | B | Physician Programming Certificate Studies | y |
| 70648 | Big data applications in Marketing | Buisness Administration | BI/User Behavior | Exposure | 1 | Lecture | A | Both | y |
| 70651 | big data management technics | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 70673 | Visualization | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
| 70677 | Global Information Systems (GIS) | Buisness Administration | GIS | Applied | 1 | Lecture | B | Both | |
| 70680 | Data Mining with R | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
| 70784 | Data Warehousing | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | |
| 70798 | Storage Systems | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 70833 | Introduced to Artificial Intelligence | Buisness Administration | AI | Applied | 1 | Lecture | B | Grad | y |
| 70949 | Data Mining and Information Disclosure | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
| 75145 | Computer Applications in Documentation and Study of Place | Geography | GIS | Applied | 1 | Lecture | B | Undergrad | |
| 75335 | Advanced GIS A | Geography | GIS | Applied | 2 | Lecture | A | Undergrad | |
| 75373 | Introduction to GIS | Geography | GIS | Applied | 1.5 | Lecture | A | Undergrad | y |
| 75967 | Python Scripting for GIS | Geography | GIS | Applied | 1.5 | Lecture | B | Grad | y |
| 80235 | *Introduction to Programming using python | Life | Programming | Applied | 1 | Lecture | A | Undergrad | |
| 80303 | Advanced Methods in Medical Image Processing | Life | Medical | Applied | 1 | Lecture | B | Both | y |
| 80376 | Matlab for Biologists | Life | Programming | Applied | 1 | Lecture | A | Both | y |
| 80392 | Computational Genomics | Life | Bio | Applied | 1 | Lecture | A | Both | y |
| 80397 | Statistics and Data Science | Life | Data Mining / Visualization | Applied | 0.5 | Lecture | A | Both | |
| 80512 | Computational Biology | Life | Bio | Applied | 1 | Lecture | B | Both | y |
| 80513 | Bioinformatics | Life | Bio | Applied | 1 | Lecture | B | Both | y |
| 80515 | Introduction to Computing | Life | Programming | Applied | 1.5 | Lecture | A | Undergrad | y |
| 80534 | Biostatistics and Introduction to Clinical Trails | Life | Bio | Applied | 1 | Lecture | B | Both | y |
| 80586 | Machine learning and applications for biological data analysis | Life | Bio | Applied | 1 | Lecture | B | Both | y |
| 80665 | Medical Informatics | Life | Medical | Applied | 1 | Lecture | B | Both | y |
| 80672 | Advanced Tools to Genome Analysis | Life | Bio | Applied | 1 | Lecture | A | Both | |
| 80675 | Clinical Informatics - Clinical Data Mining | Life | Bio | Applied | 0.5 | Lecture | A | Both | |
| 80724 | Python Programming for Scientific Research | Life | Bio | Applied | 1 | Lecture | B | Both | |
| 80725 | Deep Learning and Artificial Intelligence in Medicine | Life | Medical | Applied | 1 | Lecture | B | Both | |
| 81936 | Digital Image Processing | Medicine | Image | Applied | 1 | Lecture | B | Grad | y |
| 81958 | Text Mining for Cancer Research | Medicine | Medical | Applied | 1 | Lecture | A | Grad | y |
| 83003 | MATLAB programming and applications | Engineering | Programming | Applied | Lab | B | Undergrad | y | |
| 83011 | Workshop in Python Programming | Engineering | Programming | Core | 1 | Lecture | B | Both | y |
| 83214 | Tools for Numerical Analysis | Engineering | Math | Core | 1 | Lecture | B | Undergrad | y |
| 83216 | Introduction to Statistics and Probability | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | A | Undergrad | y |
| 83223 | Object Oriented Programming | Engineering | Programming | Core | 1 | Lecture | A | Undergrad | y |
| 83224 | Data Structures and Algorithms II | Engineering | Algorithms/Data Structures | Core | 1.5 | Lecture | B | Undergrad | y |
| 83245 | Signals and Systems | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83302 | Random Signals and Noise | Engineering | Signal Processing | Core | 1.5 | Lecture | A | Undergrad | y |
| 83320 | Digital Signal Processing I | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83321 | Statistical Algorithms for Signal Processing | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83412 | Genetics and Molecular Biology | Engineering | Bio | Applied | 1 | Lecture | B | Undergrad | y |
| 83414 | Biological data science | Engineering | Bio | Applied | 1.5 | Lecture | B | Undergrad | y |
| 83420 | Statistical Analysis of Data | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | B | Undergrad | y |
| 83456 | Design and Analysis of Algorithms | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Undergrad | y |
| 83459 | Software Engineering | Engineering | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 83620 | Information Theory | Engineering | Math | Core | 1 | Lecture | A | Both | y |
| 83622 | Introduction to Machine Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 83623 | Models and Mathematical Analysis of Networks | Engineering | Signal Processing | Core | 1 | Lecture | A | Both | y |
| 83624 | Digital Signal Processing II | Engineering | Signal Processing | Core | 1.5 | Lecture | A | Both | y |
| 83629 | Digital Image Processing | Engineering | Image | Core | 1 | Lecture | B | Both | y |
| 83633 | Digital Geometric Processing II | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
| 83635 | Reinforcement-based learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 83641 | Shape Optimization & Understanding | Engineering | Geometry | Core | 1 | Lecture | B | Both | |
| 83643 | Machine learning theory | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 83656 | Digital Processing of Geometry | Engineering | Geometry | Core | 1 | Lecture | B | Both | y |
| 83665 | Computational Biology | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
| 83666 | Control ofTheory for Biological Systems | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
| 83674 | Quantum machine learning | Engineering | Quantom | Core | 1 | Lecture | B | Both | y |
| 83676 | Data Mining | Engineering | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
| 83692 | Social networks | Engineering | Networks | Core | 1 | Lecture | B | Undergrad | y |
| 83805 | Continuous and Combinatorial Optimization | Engineering | Optimization | Core | 1 | Lecture | A | Teacher Certification | y |
| 83806 | Random Processes | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | B | Grad | y |
| 83807 | Quantum Computing | Engineering | Quantom | Core | 1.5 | Lecture | A | Grad | y |
| 83819 | Unsupervised Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 83841 | Statistical Machine Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 83843 | deep generative models | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 83867 | Probabilistic Methods and Algorithms | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | |
| 83876 | Decision Support Systems in medical imaging | Engineering | Medical | Applied | 1 | Lecture | B | Grad | y |
| 83880 | Seminar/Advanced Topics in Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | B | Grad | y |
| 83881 | Digital speech processing | Engineering | Speech | Core | 1 | Lecture | B | Grad | y |
| 83882 | Deep Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 83887 | Spatial Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | B | Grad | |
| 83888 | Computer Vision | Engineering | Image | Core | 1 | Lecture | B | Grad | y |
| 83889 | Advanced Topics in Statistical Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | A | Grad | y |
| 83900 | Discovery Theory | Engineering | Math | Core | 1 | Lecture | A | Grad | y |
| 83901 | Introduction to Data Science with Python | Engineering | ML/DL/NN | Applied | 1 | Lecture | B | Grad | |
| 83905 | Seminar/Advanced Topics in Machine Learning and Data Processing | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 83906 | Independence-based Blind Source Separation | Engineering | Statistics/Probablity | Core | #N/A | #N/A | Grad | y | |
| 83907 | Advanced topics in deep learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Grad | y |
| 83908 | Advanced topics in differential privacy | Engineering | Privacy | Core | 1 | Lecture | B | Grad | y |
| 83920 | Parallel Computation using a GPU | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Grad | |
| 83979 | Statistics and Data Analysis | Engineering | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
| 83983 | Process Modeling and Mining | Engineering | Data Mining / Visualization | Core | 1 | Lecture | B | Both | y |
| 84107 | Statistics and Probability for Chemists (SPC) | Chemistry | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
| 84190 | Introduction to Computers in Chemistry | Chemistry | Programming | Applied | 1 | Lecture | B | Undergrad | |
| 84291 | Introduction to programming in Python for chemistry | Chemistry | Programming | Applied | 1 | Lecture | B | Both | y |
| 84328 | Computational Chemistry | Chemistry | Bio | Applied | 1 | Lecture | B | Undergrad | y |
| 84846 | Introduction to Cheminformatics | Chemistry | Bio | Applied | 1 | Lecture | A | Grad | y |
| 86156 | Probability and Statistics for Physicists | Physics | Statistics/Probablity | Applied | 1.5 | Lecture | B | Undergrad | y |
| 86164 | Introduction to Computers in Physics | Physics | Programming | Applied | 1 | Lecture | A | Undergrad | y |
| 86605 | Data science for physicists | Physics | ML/DL/NN | Applied | 1.5 | Lecture | A | Both | y |
| 88151 | Computer Applications in Math | Math | Programming | Core | 1 | Lecture | B | Undergrad | |
| 88153 | Introduction to Mathematical Programming | Math | Programming | Core | 1 | Lecture | B | Undergrad | |
| 88165 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 2 | Lecture | B | Undergrad | y |
| 88170 | Introduction to Computing | Math | Programming | Core | 1.5 | Lecture | A | Undergrad | y |
| 88174 | Introduction to Object Oriented Programming | Math | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 88263 | Introduction to Statistics | Math | Statistics/Probablity | Core | 2 | Lecture | A | Grad | y |
| 88280 | Data Structures and Algorithms | Math | Algorithms/Data Structures | Core | 2 | Lecture | A | Grad | y |
| 88584 | Image Processing | Math | Image | Core | 1 | Lecture | A | Both | y |
| 88615 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 1 | Lecture | B | Both | |
| 886210 | Risk management and time series | Math | Finance/Econometrics | Core | 1 | Lecture | Summer | Both | |
| 88622 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 1.5 | Lecture | A | Grad | y |
| 88623 | Probability and Stochastic Processes | Math | Statistics/Probablity | Core | 1.5 | Lecture | B | Grad | y |
| 88624 | Analysis of Statistical Data | Math | Data Mining / Visualization | Core | 1 | Lecture | Summer | Both | y |
| 88631 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 0.5 | Lecture | A | Both | y |
| 886788 | Data Science Seminar | Math | ML/DL/NN | Core | 1 | Seminar | B | Grad | y |
| 886960 | Introduction to Programming using python | Math | Programming | Applied | 1 | Lecture | B | Undergrad | y |
| 886961 | Python Programming Workshop | Math | Programming | Applied | 1 | Lecture | A | Both | y |
| 886970 | Data Processing, Analysis and Visualization | Math | Data Mining / Visualization | Applied | 1 | Lecture | B | Undergrad | y |
| 886971 | Applied Machine Learning | Math | ML/DL/NN | Applied | 1 | Lecture | A | Both | y |
| 886972 | Big Data | Math | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
| 886980 | Networks and Complexity in the Real World | Math | Networks | Applied | 1 | Lecture | B | Grad | y |
| 88760 | Introduction to Statistics | Math | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
| 88761 | Introduction to Statistics II | Math | Statistics/Probablity | Core | 1 | Lecture | B | Grad | y |
| 88775 | Statistical Theory | Math | Statistical Theory | Core | 1.5 | Lecture | A | Grad | y |
| 88778 | Network Science | Math | Networks | Core | 1.5 | Lecture | B | Grad | |
| 88779 | Random graphs and networks | Math | Networks | Core | Lecture | B | Grad | ||
| 88780 | Supervised and Unsupervised Learning | Math | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 887810 | Introduction to artificial intelligence | Math | AI | Core | 1 | Lecture | B | Both | y |
| 88784 | Optimization | Math | Optimization | Core | 1.5 | Lecture | A | Both | y |
| 88962 | Probability and Stochastic Processes | Math | Statistics/Probablity | Core | 1.5 | Lecture | A | Undergrad | y |
| 89110 | Introduction to Computer Science | Computer Science | Programming | Core | 1.5 | Lecture | A | Undergrad | y |
| 89111 | Introduction to Object Oriented Programming | Computer Science | Programming | Core | 1 | Lecture | B | Grad | y |
| 89120 | Data Structures | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Both | y |
| 89210 | Algorithmic Programming I | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | A | Both | y |
| 89211 | Algorithmic Programming II | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | y |
| 89220 | Algorithms 1 | Computer Science | Algorithms/Data Structures | Core | 1.5 | Lecture | A | Both | y |
| 89255 | Graph Theory | Computer Science | Networks | Core | 1.5 | Lecture | A | Both | y |
| 89262 | General Probability | Computer Science | Statistics/Probablity | Core | 1 | Lecture | A | Both | y |
| 89264 | Biostatistics | Computer Science | Bio | Core | 1.5 | Lecture | A | Both | y |
| 89276 | Numeric Methods | Computer Science | Math | Core | 1 | Lecture | B | Undergrad | |
| 89312 | Programming in a multi-processor environment | Computer Science | Programming | Core | 1 | Lecture | B | Both | y |
| 89322 | Algorithms 2 | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Both | y |
| 89350 | Introduction to Communication Networks | Computer Science | Networks | Core | 1 | Lecture | A | Both | y |
| 89362 | General Statistics | Computer Science | Statistics/Probablity | Core | 1 | Lecture | B | Both | y |
| 894043 | Advanced Seminar in natural lagquage processing | Computer Science | NLP/Text | Core | 1 | Seminar | B | Both | y |
| 894044 | Research seminar in natural language processing â part 2 | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
| 89408 | Advanced Seminar in Algorithmic Game Theory | Computer Science | Algorithms/Data Structures | Core | 1 | Seminar | B | Undergrad | y |
| 894112 | User Behavior Machine Learning Algorithms Seminar | Computer Science | ML/DL/NN | Core | 1 | Seminar | B | Undergrad | y |
| 89421 | Seminar/Strategic Planning for Robots | Computer Science | Robotics | Core | 1 | Seminar | B | Undergrad | |
| 894483 | Seminar in Machine Learning and Speech Processing | Computer Science | Speech | Core | 1 | Seminar | B | Undergrad | |
| 89452 | Seminar/Web, Crowd and Big Data Management | Computer Science | DB/Big Data | Core | 1 | Seminar | A | Undergrad | y |
| 894531 | Seminar: Learning Algorithms and Natural Language Processing | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
| 89454 | Structures for Semantics | Computer Science | NLP/Text | Core | 1 | Seminar | A | Undergrad | |
| 89460 | Seminar: From Text to Information | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | |
| 89471 | Seminar in Pattern Recognition | Computer Science | Image | Core | 1 | Seminar | A | Undergrad | |
| 894851 | Advanced Seminar in Text Understanding | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | |
| 89493 | Seminar/Applied Machine Learning for NLP | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
| 89511 | Machine Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 89512 | Computational Biology | Computer Science | Bio | Applied | 1 | Lecture | B | Both | y |
| 89519 | Machine Learning for Healthcare | Computer Science | Medical | Applied | 1 | Lecture | B | Both | y |
| 89542 | Management of Big Web Data | Computer Science | DB/Big Data | Core | 1 | Lecture | A | Both | y |
| 89560 | Image Processing | Computer Science | Image | Core | 1 | Lecture | A | Both | y |
| 89570 | Artificial Intelligence | Computer Science | AI | Core | 1 | Lecture | A | Both | y |
| 89581 | Database Systems | Computer Science | DB/Big Data | Core | 1 | Lecture | B | Both | y |
| 89594 | Formal Representations for Natural Languages | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
| 89608 | Speech Recognition | Computer Science | Speech | Core | 1 | Lecture | B | Both | |
| 89641 | Topics in Information Theory | Computer Science | Math | Core | 1 | Lecture | B | Both | y |
| 89654 | Advanced Methods in Machine Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | |
| 896541 | Practical topics in Machine Learning | Computer Science | ML/DL/NN | Applied | 1 | Lecture | B | Both | |
| 89679 | Workshop in Databases | Computer Science | DB/Big Data | Core | 1 | Workshop | A | Both | y |
| 89680 | Natural Language Processing | Computer Science | NLP/Text | Core | 1.5 | Lecture | B | Both | y |
| 89685 | Introduction to Robotics | Computer Science | Robotics | Core | 1 | Lecture | A | Both | y |
| 89687 | Deep Learning Methods for Texts and Sequences | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
| 896871 | Deep learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 896872 | Deep Neural Network for Computer Vision | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | |
| 896873 | Reinforcement Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 896874 | Deep learning for perception | Computer Science | Programming | Core | 1 | Lecture | A | Grad | |
| 89688 | Statistical Machine Translation | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
| 89919 | Applied Probabilistic Models in Computer Science | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 89950 | Advanced Topics in Artificial Intelligence | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 89991 | Machine Learning Colloqium | Computer Science | ML/DL/NN | Core | Colloquium | Annual | Both | y | |
| 996728 | Law and Big Data | Law | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
| 999025 | Data Science for Jurists | Law | Data Mining / Visualization | Exposure | 1.5 | Lecture | A | Both | y |
LEGEND
Target Audience:
- Core: Students interested in understanding in depth how core data science methods and algorithms work, and in developing such methods.
Typical student profile: rigorous programming, mathematical and algorithmic skills. - Applied: Students interested in learning applied data science methods for the purpose of applying them extensively in various domains.
Typical student profile: basic programming and analytical skills, which may be acquired through relevant courses listed here for this audience. - Exposure: Students interested in high-level understanding of data science methods and their potential in various fields, in applying them using tools which do not require programming, and in social and ethical aspects of data science.
Typical student profile: students from broad disciplines who may not have programming or mathematical skills, or – for some courses – students interested in social, legal or ethical aspects of data science.
Points:
- Usually, each point corresponds to two hours in one semester.
Category:
- Fundamentals:
- Statistics/Probablity
- Programming
- Math
- Algorithms/Data Structures
- CORE
- Signal Processing
- ML/DL/NN – Machine Learning, Deep Learning, Neural Networks
- Optimization
- DB/Big Data
- Networks
- Data Mining / Visualization
- Statistical Theory
- AI – Artificial Intelligence (Agents, Problem Solving, Planning, Search algorithms, …)
- Privacy
- Game Theory
- APPLICATIONS
- NLP/Text
- Image – Image, Video Processing, Pattern Recognition
- Speech – Speech and Audio Processing
- Bio – BioInformatics, Computational Biology
- Finance/Econometrics –
- GIS – Geographic Information Systems (Geography)
- BI/User Behavior – Business Intelligence
- Digital Humanities
- Medical
- Robotics
Disclaimer