Introduction to Machine Learning

Machine Learning is everywhere. It used to be an exclusive tool used only by the giants in the industry.
Yet in our days it is used everywhere for many tasks.
From the simple tasks to the most advanced, from factories and the production line to IP based companies.
It is a must-have tool in any developer toolbox. If you want to start with, this is the course to take.

• Introduction to Machine Learning (PDF)

Overview

The course:
${\color{lime}\surd}$ Covers concepts in Machine Learning and their applications to data science tasks.
${\color{lime}\surd}$ Target developers who are required to participate in the development of an ML based application/algorithm.
${\color{lime}\surd}$ Designed to give a high-level overview of the field, the applications, and when and how to use it for System Engineers / Project Managers.

Scientific Foundations	Linear algebra, optimization, probability, Python
Supervised Learning	Classification and regression
Unsupervised Learning	Density estimation, clustering, and dimensionality reduction

Slide Samples

Goals

• The participants will be able to match the proper approach to a given problem.
• The participants will take into consideration the advantages\disadvantages of the learned methods.
• The participants will contribute to the development of ML pipeline: Processing raw data, implementing algorithms, and benchmark the results.

Pre Built Syllabus

Day	Subject	Details
1	Linear Algebra and Optimization	Vectors, norms, matrices, gradient descent
	Classification	Linear classification, support vector machine (SVM), K-nearest neighbors (K-NN)
	Performance Evaluation	Overfit/underfit, cross validation, confusion matrix, loss (risk) function, performance quality (metric and scoring): accuracy, precision, recall, and f1 score, ROC and AUC
	Feature Engineering	Feature transform, the kernel trick, feature selection
	Exercise 0	Classification and Python: numpy, scipy, pandas, matplotlib, and seabron
2	Essential Probability	Random variable\vector (discrete\continuous), expected value and higher moments, statistical dependency and correlation, Bayes’ rule
	Parametric Estimation	Maximum likelihood (ML)
	Non Parametric Estimation	Histogram, kernel density estimation (KDE), order statistics
	Statistical Classification	Logistic regression, decision trees
	Exercise 1	Classification, data analysis, visualization
3	Linear Regression	Linear least squares, polynomial fit and feature transform, R2 score
	Clustering	K-means, K-medoids, DBSCAN, hierarchical clustering (Ward’s method)
	Component Analysis	Principal component analysis (PCA)
	Non Linear Dimensionality Reduction	Multidimensional scaling (MDS), Isomaps, T-SNE
	Exercise 2	Regression, data analysis, visualization

• Introduction to Machine Learning (PDF)

Prerequisites

The course is an introductory course and as such, we try to minimize the prerequisites.
Moreover, we only assume a basic knowledge in each of the following:

• Linear Algebra.
• Basic Calculus.
• Basic Probability Theory.
• Basic Python and / or MATLAB knowledge.

Code Samples