AI/Machine Learning
Research Bootcamp

This 6-week summer project-based bootcamp will take place virtually (through Zoom) on weekdays from June 17th, 2024 to July 26th, 2024.

From June 17 – July 5 (Weeks 1-3), classes will be held every day on the weekdays from 10 am – 11 am CST.

From July 8 – July 26 (Weeks 4-6),  classes will be held every day on the weekdays from 8 am CST – 12 pm CST.

Instructors will hold optional office hours almost every evening during Weeks 1-6 and will be available on Slack 24/7 to answer questions that students have.

The camp will conclude with a conference the evening of July 25th at 8pm CST, where students will be presenting their project to parents and distinguished faculty members.

Classes will consist of a combination of lectures that will emphasize the theory and application of AI/machine learning in the sciences and digital humanities as well as group activities. 

During Weeks 4-6, lectures will take place live during the class.

During Weeks 1-3, some lectures will take place while some will be assigned for at-home watching.

Students will be assigned mini assignments almost every day, which they are expected to complete in order to participate in a machine learning research project with our partner universities.

In addition to the assignments, students are expected to spend the rest of the day working on their midterm and final project, thus making the expected commitment full-time. We do not recommend students partaking in any other major extracurricular activities during the summer.

For several of our students, school is still in session and exams are taking place during the first few weeks of our camp. We wanted to give our students additional flexibility to watch the lectures at whatever time during the day they wish, or in advance, if needed.

Additionally, the class hours can be very early for our students in PST time zone and very late for our students in Asia. Our students’ wellbeing is of utmost importance to us, and we wanted to make sure our students received enough sleep during Weeks 1-3, so that they feel ready to come to class from 8 am CST – 12 pm CST during Weeks 4-6.  

For students who are available during the morning hours, our virtual classroom will still be open so that students who are interested and available can watch the lectures together, if they wish. Instructors will also be available on Slack so that students can ask questions in real-time. Besides the one hour class each day during Weeks 1-3, there will also be office hours so students can receive additional help on homework and in understanding the lectures.

We will be covering common AI and machine learning techniques.

Students will learn AI/machine learning topics such as k-nearest neighbors, support vector machines, perceptron, classification vs regression, neural networks, convolutional neural networks, recurrent neural networks/long short-term memory networks, reinforcement learning and natural language processing.

Students will learn Python and its various machine learning and data science libraries including, but not limited to Numpy, Pandas, Scikit-learn, Tensorflow, Keras, PyTorch, Matplotlib and NLTK.

Students will learn about good coding practices, different text editors (Nano, Vim, VS Code), learn commands on Terminal/Command Line, learn how to make a virtual environment and learn how to code in a team using Github. They will also be introduced to different computing platforms such as AWS and GCP.

Students will be trained in writing a conference-style research paper in Latex and machine learning research methods. They will also be creating a technical poster.

Students will learn about ethical implications in research (bias and imbalance, etc.) and ChatGPT prompt engineering.

We will also have guest lectures from scholars from relevant fields.

Syllabus is subject to change, based on the needs of students.

Students will be working in teams of 3-4 on an interdisciplinary mid project and final project, in which they will apply machine learning to a specific field. All students will work on the same mid-project and each group will be approaching the problem statement in a unique way. The final project will be unique to each group, and students will work on the project independently or in collaboration with a university.

In a rapidly changing world, it is very important to us at MehtA+ that our students work on interdisciplinary research since it demonstrates to universities and companies that our students have the potential to keep an open-mind, adapt and apply unique solutions to new challenges.

We do! Check out all the past years’ projects underneath Student Projects here. 

If you’re not sure which project to check out first since they all look very interesting, maybe check out Deep Learning Techniques for Melanoma Classification which uses some machine learning techniques covered in graduate school!

We work with top universities all across the world! In the past, we have worked with universities in the US such as Williams College, Haverford College and University of Buffalo SUNY as well as international universities such as University of Bologna, Italy University of Groningen, Netherlands and Singapore Institute of Technology.

Within the universities, we have worked with a multitude of labs including language, digital humanities and operations research labs, as well as university art museums and conservation-related non-profits created out of university labs. You can view the universities we have worked with in the past here.

The universities we work with are always excited to see how MehtA+ students apply machine learning to their research.

Our instructors are all experienced machine learning research researchers who are graduates of prestigious universities such as MIT and Stanford. Besides teaching at MehtA+, they are currently pursuing their PhD studies in computer science and industry professionals at top software companies, where they are working with large language models (GPT) and other cutting-edge machine learning models. 

Our TAs are alumni of this bootcamp, who are currently pursuing computer science at top-tier universities.

We think they are, but you be the judge! Check out the article the head instructor wrote on the AI/Machine Learning Research Bootcamp. Ferrero Rocher, anyone?

Our student-to-MehtA+ staff ratio is around 5:1. In addition to working with MehtA+ staff, students will be working closely with PhD students and professors from various universities.