Course Identification

Title:

Introduction to Big Data and Machine Learning

Code:

20213121

Lecturers and Teaching Assistants

Lecturers:

Prof. Zohar Yakhini , Dr. Keren Ouaknine, Prof. Ariel Shamir

TA's:

N/A

Course Schedule and Location

Year:

2021

Semester:

First Semester

When / Where:

N/A

First Lecture:

04/11/2020

Field of Study, Course Type and Credit Points

Life Sciences (Computational and Systems Biology Track): Lecture; Obligatory; Core; 2.50 points

Comments

Class will take place 14:00-16:30 every Wednesday at IDC.
Recitation will take place 1700-1800 and the course staff will be available for consultation after that (office hours), until 1900.
The first class will take place on 4/Nov/2020 and the last class will take place on 27/Jan/2021, will be 13 consecutive weeks.

Prerequisites

Basic programming course + basic calculus and linear algebra

Restrictions

Participants:

20

Language of Instruction

English

Registration by

Registration By:06/10/2020

Attendance and participation

Obligatory

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Assignments:

60%

Final:

40%

Project

Evaluation Type

Final assignment

Scheduled date 1

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Estimated Weekly Independent Workload (in hours)

N/A

Syllabus

Part 1 - Introduction to Machine Learning

Introduction, linear regression
Python libraries: pandas, numpy, visualization libraries
Evaluation, training and test sets, ROC curves
Classification
Clustering, PCA

Part 2 - Data science and Statistics

Density estimation, MLE, Bayes classification
Statistics for scientists: correlations, p-values and multiple testing
A mini project in analyzing high throughput data

Part 3 - Big Data

Introduction to Hadoop
Query Languages
Machine Learning use cases over Big Data

Part 4 - Presentation of the mini project results

Learning Outcomes

Upon successful completion of this course students should be able to:

- understand machine learning algorithms and apply them to data

- statistically asses observations in data including correlations

- launch and use Big Data platforms to analyze large volumes of data

- understand and configure machine learning packages including Deep Learning and SVM

- analyze large volumes of experimental data and present results

Reading List

An introduction to statistical learning by James and co.

Pattern recognition and machine learning by Bishop and co.

Website

N/A