Machine Learning and Neural Networks for Neuroscience
Course No: 27-504-01/02
Lecturer Name: Dr. Zvi Roth
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Course Type: |
Lecture |
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Scope of credits: |
2 credit points + 1 credit point training |
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Year of study: |
Graduate students |
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Semester: |
A |
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Day & Time: |
TBA |
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Reception Time: |
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Lecturer Email: |
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Moodle Site: |
TBA |
Course description and learning goals
Course Abstract
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn a model given a large amount of data in a way that resembles human learning. Neural networks (NNs) are a type of machine learning model inspired by the structure and function of the neural networks inside the human brain. In recent years, ML and in particular NNs have become very popular in many domains, and have been proved extremely effective in a range of applications, from machine vision and speech recognition to decision making and robotics.
Learning objectives
After completing the course students should understand the mathematical basis of Machine Learning, and be able to apply relevant Machine Learning techniques to real-world problems.
Knowledge
- Learners will be familiar with core Machine Learning methods.
- Learners will be able analyze the mathematical basis of different Machine Learning models.
- Learners will be aware of the pros and cons of various Machine Learning techniques.
Skills
- Learners will be able to choose appropriate Machine Learning techniques to analyze data.
- Learners will be able to implement basic Machine Learning models in real-world situations.
Active learning – lessons plan
You can plan an active learning process for the entire course or listfor each active learning activity lesson in the following table:
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Lesson No. |
Topic |
Active learning |
Required reading |
Assessment |
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1 |
Introduction |
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2 |
Linear regression |
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3 |
Classification, Regularization |
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4 |
Generalized linear models |
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5 |
Generative algorithms, Model evaluation |
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6 |
Decision Trees |
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7 |
Support vector machines |
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8 |
Kernels |
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9 |
Artificial neural networks: Perceptron, MLP, Backpropagation |
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10 |
Unsupervised learning |
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11 |
Autoencoders |
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12 |
Generative adversarial networks |
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13 |
Natural language processing |
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14 |
Review |
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(In a course that lasts a whole year, the additional sessions should be added)
* There may be changes in the syllabus depending on learning progress and effectiveness
Final grade
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Description of the learning product |
Weight in the final score |
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Final project |
Will account for 45% of the final grade |
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Home assignments |
30% of final grade |
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Midterm exam |
25% of final grade |
Course requirements
Students must submit 80% of home assignments, and the final project.
Prerequisites
- Mathematics: Probability and statistics, Linear Algebra, Calculus.
- Fluency in a programming language preferably Python.
Bibliography: Up-to-date reading, viewing, and listening content items
- Machine Learning and Pattern recognition, C. Bishop (2006)
- Deep Learning, I. Goodfellow and Y. Bengio (2015)
- The Elements of Statistical Learning, T. Hastie et al. (2001)
