Basic Information
Statistical Methods For Data Classification By Examples, Pac Learning, Vc Dimension, Nearest Neighbors Algorithm, Decision Trees, Linear Separators, Svm Algorithm and The Kernel Method, Convex Optimization With Gradient Descent and Stochastic Gradient Descent, Validation And Model Selection, Generative Models and Naive Bayes, Ensemble Methods, Feature Generation and Selection, Neural Nets, Unsupervised Learning# Dimensionality Reduction, Clustering. Learning Outcomes# By The End Of The Course, The Student Will Be Able# 1. to Prepare Data For Machine Learning# Feature Generation, Feature Selection, Separation to Training, Testing and Validation. 2. to Use Common Supervised and Unsupervised Learning Algorithms, While Using Standard Software Packages and Real Datasets. 3. to Understand The Mathematical Principles Underlying The Taught Material, Including Sample Complexity, Computational Complexity, Overfitting, Under Fitting, Pac Learning, Vc Dimension and The Limitation of These Theories.
Faculty: Computer Science
|Graduate Studies
Pre-required courses
(94412 - Probability (advanced) and 234125 - Numerical Algorithms) or (104034 - Introduction to Probability H and 234125 - Numerical Algorithms)
Course with no extra credit
35054 - Gne Hcem Rof Ia ¸ Gninrael Enicham Ortni 46195 - Machine Learning 236766 - Introduction to Machine Learning
Related Books
- Machine learning : a probabilistic perspective - Murphy, Kevin P.
- Pattern classification - Duda, Richard O.
- Pattern recognition and machine learning - Bishop, Christopher M.
- The elements of statistical learning : data mining, inference, and prediction - Hastie, Trevor
- The Elements of Statistical Learning Data Mining, Inference, and Prediction - Hastie, Trevor.
- Understanding machine learning : from theory to algorithms - Shalev-Shwartz, Shai
Semestrial Information
Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 2 Lab Hours
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Responsible(s)
Nir Rosenfeld
Exams
Session A: 03-09-2024 Session B: 10-10-2024Registration Groups
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 2 Lab Hours
Go to Course Page
Responsible(s)
Yonatan Belinkov
Exams
Session A: 12-04-2024 09:00 - 12:00- אולמן 101. 102. 103. 104. 105. 200. 201. 202. 203. 205. 206. 307.
- טאוב 3. 4. 5. 6. 7. 8. 9.
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 2 Lab Hours
Go to Course Page
Responsible(s)
Nir Rosenfeld
Notes
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הקדמים נאכפים ללא יוצאי דופן! כקדם בהסתברות יתקבל כל קורס מבוא להסתברות או מבוא להסתברות וסטטיסטיקה. כתחליף לאלגו. נומריים יתקבל כל קורס באנליזה נומרית או קורס מבוא לאופימיזציה בלבד בחלק מהתחליפים האלה לא מתמקדים בדיוק באותם נושאים ובאחריות הסדטונטים להשלים פערים.
Exams
Session A: 16-07-2023 09:00 - 12:00- טאוב 4. 5. 7. 8. 9.
- טאוב 3. 5. 6. 7.
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