Difference between revisions of "MehtA+ Machine Learning Bootcamp"

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===Locations and Dates===
 
===Locations and Dates===
  
Online: This 6-week summer project-based bootcamp will take place virtually (through Zoom) on weekdays from June 19th, 2023 to July 28th, 2023.
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Online: This 6-week summer project-based bootcamp will take place virtually (through Zoom) on weekdays from June 17th, 2024 to July 26th, 2024.
  
 
===Camp Description===  
 
===Camp Description===  
  
MehtA+ AI/Machine Learning Research Bootcamp will be taught by MIT and Stanford alumni.
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This virtual 6-week university-level camp taught by MIT and Stanford engineers for 8th – 12th graders starts from the basics. Students are first introduced to the mathematics behind various AI, machine learning and deep learning models and then taught various data preprocessing techniques and how to train their own complex AI models from scratch. By the end of the camp, students will have acquired the basic skills needed for an entry-level data scientist and machine learning engineer position.
 
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This six-week interdisciplinary summer bootcamp will provide students with expert instruction in cutting edge university-level AI/Machine Learning, which students will then apply to projects run by professors from top US and international universities.  This online collaborative course teaches high school students advanced computer science skills, requiring no prior knowledge.  
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For their midterm and final project, students work in teams of 3-4 and engage in cutting edge interdisciplinary machine learning research either in conjunction with a university or independently, in fields such as medicine, digital humanities, economics and linguistics. Upon conclusion of the project, students write and publish their research papers online and present their research posters to university professors in the annual MehtA+ Machine Learning conference and have the potential to publish in research journals and present in other conferences as well. Students who successfully complete the camp will receive a certificate.
 
 
Students are first introduced to the mathematics behind various AI, machine learning and deep learning models and then taught various data preprocessing techniques and how to train their own complex AI models from scratch and work in small groups for a midterm project. For their final project, each group of students is paired with top university scholars working on projects that require machine learning. Students will potentially receive an opportunity to have their work published in professional academic journals.
 
  
 
===Application===  
 
===Application===  
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Application here: https://mehtaplustutoring.com/ai-ml-research-bootcamp-application/
 
Application here: https://mehtaplustutoring.com/ai-ml-research-bootcamp-application/
  
Deadline: Wednesday, June 7, 2023 at 11:59 pm CST, or whenever slots fill up!
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Deadline: Wednesday, June 5, 2024 at 11:59 pm CST, or whenever slots fill up!
  
 
===Daily Schedule===
 
===Daily Schedule===
  
Class will be held every day on the weekdays from 8am CST - 12 pm CST. Classes will consist of a combination of lectures that will emphasize the theory and application of AI/machine learning in the humanities as well as group activities.  
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From June 17 – July 5 (Weeks 1-3), classes will be held every day on the weekdays from 10 am – 11 am CST.
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From July 8 – July 26 (Weeks 4-6),  classes will be held every day on the weekdays from 8 am CST – 12 pm CST.
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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.
 +
 
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===Format===
 +
 
 +
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.  
  
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.
+
During Weeks 4-6, lectures will take place live during the class.
  
In addition to the assignments, students are expected to spend the rest of the day working on their final project, thus making the expected commitment full-time. We do not recommend students partaking in any other major extracurricular activities during the summer.
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During Weeks 1-3, some lectures will take place while some will be assigned for at-home watching.
  
The camp will conclude with a conference the evening of July 27th at 8pm CST, where students will be presenting their project to parents and distinguished faculty members.
+
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.
  
Instructors will hold optional office hours almost every evening and will be available on Slack 24/7 to answer questions that students have.
+
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.
  
 
===Syllabus===
 
===Syllabus===
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We will be covering common AI and machine learning techniques.
 
We will be covering common AI and machine learning techniques.
  
Students will learn AI/machine learning topics such as rule-based systems, k-nearest neighbors, support vector machines, perceptrons, classification vs. regression, artificial neural networks, convolutional neural networks, Markov decision processes, reinforcement learning, recurrent neural networks/long short-term memory networks, natural language processing, generative adversarial networks, k-means, collaborative filtering, PCA and t-SNE, GPT-3 and DALL-E 2.
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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, NLTK, SpaCy, Gensim, Matplotlib, and SciPy, as well as web development/research-paper writing skills like HTML, CSS, Javascript, and Latex.
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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 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 be taught HTML, CSS and JavaScript in order to create a website to display their findings. They will also be creating a technical poster and video demo.
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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.).  
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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.
 
We will also have guest lectures from scholars from relevant fields.
  
 
Syllabus is subject to change, based on the needs of students.
 
Syllabus is subject to change, based on the needs of students.
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 +
===Projects===
 +
 +
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.
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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.
  
 
===More Information + FAQs===
 
===More Information + FAQs===
  
 
For more information, examples of past midterm and final projects, and FAQs, please visit: https://mehtaplustutoring.com/ai-ml-research-bootcamp/
 
For more information, examples of past midterm and final projects, and FAQs, please visit: https://mehtaplustutoring.com/ai-ml-research-bootcamp/

Revision as of 19:19, 15 January 2024

MehtA+ Machine Learning Bootcamp is held virtually, every summer for 8th-12th graders (ages 13-18) located anywhere in the world interested in learning university-level computer science concepts (programming, AI/machine learning and data visualization) and their application to projects in fields such as medicine, digital humanities, economics and linguistics. Students complete a research project with like-minded individuals under the guidance of faculty members of prestigious universities.

Locations and Dates

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

Camp Description

This virtual 6-week university-level camp taught by MIT and Stanford engineers for 8th – 12th graders starts from the basics. Students are first introduced to the mathematics behind various AI, machine learning and deep learning models and then taught various data preprocessing techniques and how to train their own complex AI models from scratch. By the end of the camp, students will have acquired the basic skills needed for an entry-level data scientist and machine learning engineer position.

For their midterm and final project, students work in teams of 3-4 and engage in cutting edge interdisciplinary machine learning research either in conjunction with a university or independently, in fields such as medicine, digital humanities, economics and linguistics. Upon conclusion of the project, students write and publish their research papers online and present their research posters to university professors in the annual MehtA+ Machine Learning conference and have the potential to publish in research journals and present in other conferences as well. Students who successfully complete the camp will receive a certificate.

Application

Application here: https://mehtaplustutoring.com/ai-ml-research-bootcamp-application/

Deadline: Wednesday, June 5, 2024 at 11:59 pm CST, or whenever slots fill up!

Daily Schedule

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.

Format

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.

Syllabus

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.

Projects

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.

More Information + FAQs

For more information, examples of past midterm and final projects, and FAQs, please visit: https://mehtaplustutoring.com/ai-ml-research-bootcamp/