Course Identification

Title:

Introduction to Big Data and Machine Learning

Code:

20223321

Lecturers and Teaching Assistants

Lecturers:

Leon Anavy, Prof. Zohar Yakhini

TA's:

Alon Oring, Ben Galili

Course Schedule and Location

Year:

2022

Semester:

First Semester

When / Where:

Wednesday, 14:15 - 16:30

Tutorials
Wednesday, 17:15 - 18:00,

First Lecture:

27/10/2021

End date:

18/03/2022

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 14:15-16:30 every Wednesday at IDC.
Recitation will take place 17:00-18:00 and the course staff will be available for consultation after that (office hours), until 19:00.
The first class will take place on 27/Oct/2021 and the last class will take place on 26/Jan/2021.

Prerequisites

Basic programming course + basic calculus and linear algebra

Restrictions

Participants:

20

Language of Instruction

English

Registration by

Registration By:17/10/2021

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
Advanced statistical methods: non-parametric tests,
A mini project in analyzing high throughput data

Part 3 - Class Workshops

NGS Data Analysis
End-to-end data analysis and model building.

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