Under construction, subject to change
applied machine learning
למידת מכונה יישומית
לתלמידי תואר ראשון ושני
202.2.6511, Spring 2022 סמסטר ב
Lecturer: Prof. Moshe Sipper
Syllabus
This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python. Knowledge of Python is helpful (קובץ הקורסים).
Contents:
This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python. Knowledge of Python is helpful (קובץ הקורסים).
Contents:
- What is machine learning?
- (possibly basic Python programming)
- Applying machine learning: evaluation, dataset splits, cross-validation, performance measures, bias/variance tradeoff, visualization
- Supervised learning: models, features, objectives, model training, overfitting, regularization, classification, regression, gradient descent, k nearest neighbors, linear regression, logistic regression, decision tree, random forest, adaptive boosting, gradient boosting, support vector machine
- Dimensionality reduction: principal component analysis
- Artificial neural networks: perceptron, multi-layer perceptron, backpropagation, deep neural network, convolutional neural network, GANs
- Unsupervised learning: clustering, k-means, autoencoder
- Evolutionary algorithms: genetic algorithm, genetic programming
Administrative Details
- Prerequisites: Data structures, Algorithms, Linear algebra, Probability & statistics
- Highly recommended prerequisite: Python programming
- Credits: 4
- Grade:
- 70%: Final exam
- 30%: Homework (probably 3 coding assignments)
- You must pass both course components in order to pass the course.
Lesson plan
- TBD