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Intro to Applied Machine Learning in Life Sciences with R

Apply for our course. Limited spots available.

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Online

Live sessions

Sessions will be recorded

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5 Weeks

April 1st - April 29th

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Evenings

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5pm-6pm

$990

Tailored curriculum and resources

Spring 2024 Intake

Application Deadline: March 27th, 2024

Rolling Admission

Overview

This course is tailored to those looking to learn fundamental machine learning concepts and apply it in the context of the life sciences. 

To be successful in the course, students will spend 5-10 hours/week on homework and readings.

Designed for those who are proficient in R and statistics.  

Abstract Futuristic Background

Why our Course?

Distinguishing Features

01

Small Cohort Size

Our data science class is designed with a small cohort size to ensure a highly interactive and personalized learning experience. This approach fosters stronger connections among students and instructors, enabling in-depth discussions, tailored support, and a collaborative learning environment that’s hard to find elsewhere.

03

Real-time/Personal Feedback

We believe in the power of immediate, personalized feedback to accelerate learning. Our instructors provide real-time responses to questions and assignments, offering constructive criticism and guidance to help students refine their skills, adjust their learning strategies, and continuously improve their performance throughout the course.

02

Graded Problem Sets

Each module in our curriculum features meticulously crafted graded problem sets that challenge students to apply what they've learned in practical, real-world scenarios. These assignments are designed to deepen understanding, enhance problem-solving skills, and prepare students for the complexities of data science projects in their future careers.

04

Talks from Industry Veterans

Our class offers exclusive talks from seasoned industry veterans, providing invaluable insights into the data science field. These sessions cover a range of topics, from emerging technologies and trends to career advice and personal growth stories. It's a unique opportunity for students to learn from experts who've made significant contributions to the industry.

Syllabus & Schedule

Module 1

Intro to Machine Learning

Learn foundational concepts and techniques essential for understanding and applying ML algorithms. We will dive into the basics of machine learning, exploring key evaluation metrics such as accuracy, precision, recall, and F1 score, which are crucial for assessing algorithm performance. Through this module, students will gain a solid understanding of how to select and apply the appropriate evaluation metrics for various ML models, setting the stage for more advanced topics in machine learning.

Module 2

Project I: Image Analysis

Put into practice what you learned from Module 1 with a project that explores image analysis using ML algorithms.

Module 3

Principle Component Analysis

Learn PCA in ML and how it is a powerful technique to simplify complex datasets to enhance data interpretability while perserving essential information.

Module 4

Project II: RNA-Seq Analysis

Apply your new machine learning skills and techniques from the course to explore and analyze RNA-Seq data.

What You'll Gain

You will learn fundamental ML concepts and how to apply them. Hear from industry veterans within the space to understand what you need to succeed in the life sciences. 

Training & Testing

Evaluation Metrics

ML Algorithms

Industry Experts

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Instructor

Professor Rafael Irizarry

Rafael Irizarry is currently a Professor and Chair of the Department of Data Science at the Dana-Farber Cancer Institute and a Professor of Biostatistics at Harvard School of Public Health. 

Rafael is well-known for his work in genomics, specifically the analysis of high-throughput technologies data. His contributions to open-source statistical methodologies have positioned him as one of the most highly cited scientists in his field.

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“The case studies and relevant examples were incredibly useful to put theory into context and practice."

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