Machine learning is having profound effects in many different industries, from financial services to retail to advertising. It is fast becoming a fundamental tool for making better decisions in business—decisions driven by data, not gut feelings or guesswork.
Today, every business has access to reams of data, whether it’s operational data, customer data, third party data, or supplier data. The question for decision makers becomes: How can I use this data to make informed predictions, take action, and evaluate the outcomes for future decision making?
Massachusetts Institute of Technology (MIT) is at the forefront of research and practice for this emerging field within data science. Professor Devavrat Shah, who leads the Department of Statistics and Data Science at MIT, takes learners on a deep dive into what’s possible with machine learning.
This online program takes a look at machine learning through a lens of practical applications and requires no prerequisites in terms of math or computational sciences, although some basic experience with statistics is helpful. It is designed specifically for decision-makers who want to develop a competitive edge by harnessing the power of machine learning.
Regardless of where you are on the spectrum of machine learning adoption, and more broadly artificial intelligence, this online program provides the latest thought leadership in machine learning tools and techniques.
Spending on artificial intelligence and machine learning is forecast to grow from $12 billion in 2017 to $57.6 billion by 2021, according to International Data Corporation (IDC).
Who is this program for?
Decision makers across all business and technical functions will gain a practical understanding of the tools and techniques used in machine learning applications for business. Examples from retail, e-commerce, financial services, healthcare, social media, advertising, technology, gaming, and pharmaceuticals are included in this online program. However, participants from all industries and sectors will find these examples relevant.
Functional and cross-functional teams are encouraged to attend together, to accelerate the machine learning adoption process.
Your learning journey
The program takes you through a progression of the four building blocks of machine learning. In addition to the practical knowledge that you’ll gain from the video lectures, you’ll learn from live webinars with faculty, peer discussions, case studies, assignments, and quizzes. Throughout the journey, there are real-life machine learning facilitators to provide guidance and clarification. At the end of the program, you should be prepared to apply some of these techniques at your own organization.
- Visualizing One-dimensional & Multi-Dimensional Data
- Using PCA, Clustering, K-Means, and Topic Models
- Structured vs. Unstructured Data
- Supervised Learning
- Neural Networks
- Optimal Decision in Presence of Uncertainty
- Dynamic vs. Static Environment
- High to Low Information Rate
- Using Model Predictive Control, Markov Decision Process, Multi-Armed Bandit, and Reinforcement Learning
- Cause and Effect Relationship
- Thoughtful Experience Design
- Randomized Control
- Hypothesis Testing
- Synthetic Control
- Time-Series Forecasting
Note: MIT Professional Education reserves the right to change the curriculum and delivery of the program to improve the participant experience.
What you'll learn
The tools and techniques in this machine learning program can help to address many common business challenges. Learn with examples from:
How do you predict whether a borrower will default on a loan?
How do you know which marketing channel is performing best, and what is the interaction effect when you are using multiple channels?
How are data scientists exploring ways to predict the future price of digital assets, such as Bitcoin?
When developing new drugs, how can you design better experiments to know if a new drug will be more effective than an existing one?
To optimize your inventory, how do you know whether to pull from a distribution center or a retail store to fulfill an online order?
How do you decide when to cross-market vs. upsell a customer at checkout —what’s drives more revenue?
Module 1: Introduction and Overview of Machine Learning
Overview of the four building blocks of machine learning:
- Understanding data: What is it telling us?
- Prediction: What will happen?
- Decision making: What to do?
- Causal inference: Did it work?
Module 2: Understanding Your Data
Learn the basic characteristics of data sets and identify effective statistical tools and visualizations to glean insights from your data.
- Ask the right questions of the data
- Know which tools to use to unlock insights
- How data visualization clarifies data
Application: Wikipedia and Online Marketplaces, Amazon, and Etsy.
Module 3: Prediction Part 1 – Regression
Understand the basic concept of linear regression and how it can be used with historical data to build models that can predict future outcomes.
- How to build a model that fits best with your data
- How to quantify the degree of your uncertainty
- What to do when you don’t have enough data
- What lies beyond linear regression
Application: Designing a marketing campaign.
Module 4: Prediction Part 2 – Classification
Classification is used to predict outcomes that fall into two or more categories, such as male/ female, yes/no, or red/blue/green.
- Compare the ability of different methods to minimize prediction errors
- Make better predictions, based on your data and desired outcome
- Use the right approaches to deal with data complexity
Application: Spam filters, detecting malicious network connections, and predicting credit defaults among borrowers.
Module 5: Prediction Part 3 – Neural Networks
Neural networks are much like the networks in the human brain. They are used in machine learning to model complex relationships between inputs and outputs and to find patterns in data.
- What are neural networks and how do they work?
- Explore the history and examples of simple and complex neural networks
- How neural networks minimize errors, regardless of the size of your data set
Application: Improving online search in e-commerce websites.
Module 6: Decision-Making Foundations
Decision making is about selecting the “optimal” decision or action in the presence of uncertainty, which all business and technical leaders face regularly.
- Learn how the decisions you make impact the immediate future and beyond
- Choose the right approach based on the environment, the rate of information flow, and your goal
- Strike the balance between exploration (identifying what we don’t yet know) with exploitation (using what we already know)
Application: Retail planning, website design, and creating a perfect chess player.
Module 7: Decision-Making Applications
Focus on many of the practical applications of decision making, from static to dynamic situations.
- Learn about making real-time recommendations for customers to drive more business
- Learn how to achieve optimal inventory management
- See how data scientists are exploring ways to predict the future price of digital assets such as Bitcoin
Application: Deciding on routes to work and deciding how to make investments in fast-changing environments.
Module 8: Causal Inference
Casual inference is about understanding the relationship between cause and effect.
- Given a set of observations, identify what caused those observations
- Learn to design experiments that deliver meaningful conclusions
Application: Assess whether discount coupons caused higher sales of certain products, or whether some external factor, such as an improved economy, was the reason for the increased sales.
Get recognized! Upon successful completion of the program, MIT Professional Education grants a certificate of completion to participants. This program is graded as a pass or fail; participants must receive 80% to pass and obtain the certificate of completion.
4.8 Continuing Education Units Awarded
Special achievement award
MIT Professional Education Fire Hydrant Award
It’s said that studying with MIT is like drinking from a fire hose—intense, immensely satisfying. For those participants who demonstrate leadership by going above and beyond in the program, they’ll receive the coveted Fire Hydrant Award. This award can be displayed in professional bios, such as on LinkedIn. Decisions are made by MIT program faculty and facilitators based on participation and behaviors that exemplify exceptional leadership and contribution to the overall program experience for the cohort.
- Duration: 2 months, online 6-8 hours/week
- Program fees: $1,950
Program taught in: