2 Week Machine Learning Workshop

Our public discord: https://discord.gg/zMEsvS2h8b
Post all questions here!

Welcome to the Machine Learning Workshops at Hacker Dojo! This course is designed to provide participants with a comprehensive understanding of machine learning concepts and practical skills. Whether you are a beginner or have some experience, our workshops will guide you through the essentials of machine learning, from data preprocessing to model deployment.
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SECURE YOUR SPOT
Tickets: $90/person
Submit payment here: https://buy.stripe.com/aEU5mK4E6dGHfiocMT

Accessibility for people with limited financial support:
Free for up to 5 students based on evidence of volunteering, evidence of strong technical interest in machine learning, and reason for financial support. Discretion is the decision of the person with Administrative responsibility for the course.

In the spirit of maintaining inclusivity at Hacker Dojo, we’ll offer an additional way to attend this course. If the cost is a barrier, attendees can submit a 1-page essay explaining why they should attend the event for free and how it will benefit their future endeavors. We want to ensure that everyone has the opportunity to participate, regardless of financial constraints.
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### Goals

• Develop a Strong Foundation: Equip participants with a solid understanding of fundamental machine learning concepts and techniques.
• Hands-On Learning: Engage in interactive sessions with real-world datasets to build and evaluate machine learning models.
• Collaborative Environment: Work alongside fellow participants in a supportive community, fostering collaboration and knowledge sharing.
• Tool Proficiency: Ensure familiarity with popular machine learning tools and frameworks, such as PyTorch and scikit-learn.
• Collaboration and Networking: Encourage collaboration among participants and provide opportunities for networking with industry professionals.
• Portfolio Development: Create a portfolio of projects completed during the workshops to demonstrate your machine learning skills to potential employers. This portfolio will include detailed documentation, code samples, and results from various machine learning tasks, showcasing your ability to tackle real-world problems and your proficiency with industry-standard tools and techniques.

### Prerequisites

• Basic Python Skills (We can help on-board after class!)
• Interest and curiosity about Machine Learning and Deep Learning

### Technologies

Onboarding with dev environment help available after class!

• Python
• PyCharm
• Jupyter Notebooks

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## Instructors

• Gintautas Švedas: Gintautas is an Information Technology graduate from Vilnius University, Lithuania. He now works as an AI engineer at SAP. Previously, he worked with nuclear research at CERN, Switzerland, not to mention Scandinavian banks and contractor roles. He has developed various software and AI solutions, such as prompt engineering, vector databases and chatbots.
• John Hanley: has guided startups such as Oracle and Yahoo through rapid growth. He worked for eight years as a researcher in Xerox PARC's machine learning group, helping diverse industry and government partners solve their business problems.
• Anudha Mittal: held contract positions in data science and model implementation at Kaiser Permanente and Caterpillar. She previously published mathematical findings in Ultramicroscopy, Microscopy and Microanalysis, Physical Review B, and Nuclear Engineering and Technology; in addition she has published experimental work and worked professionally at the Naval Nuclear Lab as a materials engineer. She has also supported software development at a microscopy start-up, ZoNexus.

## Lectures

Our workshop series is structured into a series of lectures, each focusing on a key aspect of machine learning.
Below is an outline of the lectures:

1. Introduction to Machine Learning

• Tue., 2024-12-03
• Overview of machine learning and its applications
• Team formation
• First paper presentation

2. Data Preprocessing and Exploration

• Wed., 2024-12-04
• Types of machine learning: supervised and unsupervised
• Techniques for cleaning and preparing data
• Exploratory data analysis and visualization

3. Supervised Learning Algorithms

• Thu., 2024-12-05
• Understanding regression and classification
• Hands-on with algorithms like linear regression, decision trees, and support vector machines

4. Neural Networks and Deep Learning

• Tue., 2024-12-10
• Basics of neural networks and deep learning
• Building and training neural networks with TensorFlow or Keras
• Second paper presentation

5. Unsupervised Learning and Clustering

• Wed., 2024-12-11
• Introduction to clustering and dimensionality reduction
• Practical applications of k-means and PCA

6. Model Evaluation; Team Presentations

• Thu., 2024-12-12
• Techniques for evaluating model performance
• Hyperparameter tuning and model optimization strategies
• Teams present their results

Each lecture is designed to build upon the previous one, ensuring a comprehensive understanding of machine learning. Participants will have the opportunity to work on hands-on projects and real-world datasets, reinforcing the concepts learned in each session.