Course Identification

Title:

Introduction to Machine Learning and Statistics

Code:

20233011

Lecturers and Teaching Assistants

Lecturers:

Prof. Zohar Yakhini

TA's:

Shulamit Finley, Alon Oring, Ben Galili, Leon Anavy

Course Schedule and Location

Year:

2023

Semester:

First Semester

When / Where:

Wednesday, 14:15 - 18:00

First Lecture:

09/11/2022

End date:

03/02/2023

Field of Study, Course Type and Credit Points

Life Sciences (Computational and Systems Biology Track): Lecture; Obligatory; Core; 3.00 points
Life Sciences: Lecture; Elective; Regular; 3.00 points

Comments

The course is taking place on Wednesdays at IDC, Herzliya.
Classes will take place from 14:15-16:45.
Recitations will take place from 17:15-18:00.
The course staff will be available for consultation after that (office hours), until 19:00.
Final projects will be presented on February 28th at 14.
NOT OPEN FOR AUDITORS

Prerequisites

Basic programming course + basic calculus and linear algebra

Restrictions

Participants:

20

Language of Instruction

English

Registration by

Registration By:23/10/2022

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

Final projects will be presented on February 28th at 14.

Estimated Weekly Independent Workload (in hours)

N/A

Syllabus

Part 1 - Introduction to Machine Learning

Introduction, regression and classification problems
Linear regression
Evaluation, training and test sets, ROC curves
Decision Trees, Random Forests
Linear Classifiers, Support Vector Machines
Unsupervised Learning: Dimensionality reduction, Clustering

Python libraries: pandas, numpy, visualization libraries, sklearn

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 pipelines and Data Analysis

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 assess observations in data including correlations

- 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