Machine Learning Fundamentals
This is your go-to page for everything related to the Machine Learning Fundamentals (MLF) Program by UTMIST. Find all the essential links in one place—from the IBM platform for notebooks and tools, to lecture recordings and key resources to help you stay on track and succeed throughout the program.
Phase 1
Learn basics of ML through workshops
Collaborative learning with peers
Build foundational knowledge and theory
Phase 2
Apply knowledge through building a project
Access mentoring and office hours
Gain hands on experience and skills in ML
Phase 1: Workshop Schedule
New lectures, slides, and labs will be uploaded weekly! Join us for synchronous lectures weekly starting September 27 from 1-3pm in BA1160!
Introduction to Machine Learning
Week 1 - Sept 27, 2025What is ML, types of ML, applications, linear regression basics, PyTorch introduction
Logistic Regression
Week 2 - Oct 4, 2025Classification with sigmoid, decision boundaries, model evaluation, train-validation-test split
Neural Networks Part 1
Week 3 - Oct 11, 2025Perceptrons, activation functions, forward propagation, limitations of linear models
Neural Networks Part 2
Week 4 - Oct 18, 2025Backpropagation intuition, optimization algorithms, training loops, practical tips
Decision Trees & Ensembles
Week 5 - Nov 8, 2025Split criteria, tree depth, random forests, bagging, entropy & information gain
Naive Bayes
Week 6 - Nov 15, 2025Probability basics, Bayes' theorem, generative vs discriminative models, text classification
Best Practices & Evaluation
Week 7 - Nov 22, 2025Baseline models, bias-variance tradeoff, evaluation metrics, iterative ML process
Deep Learning & Modern Architectures
Week 8 - Nov 29, 2025Why deep learning, CNNs, RNNs, transformers, practical implementation with PyTorch
Phase 2: Project
Check back for more information!
Frequently Asked Questions
For any other questions please reach out to the team at UTMIST!
Basic knowledge of Python programming and high school mathematics (algebra, calculus) is recommended. No prior machine learning experience is required.
Yes, please bring your own laptop. We'll be using PyTorch and other tools that need to be installed on your device.
All materials, slides, and recordings will be available online. You can catch up on missed content and reach out to the team for any questions.
No, this workshop is completely free for UTMIST members. It's part of our commitment to making AI and ML education accessible.
Yes, participants who complete all 8 workshops will receive a certificate of completion from UTMIST.
Yes, non-members are welcome to attend. We encourage you to join UTMIST to access additional resources and events.
We'll primarily use PyTorch for deep learning, along with Python, Jupyter notebooks, and various visualization libraries.
Each workshop session is approximately 2 hours long, including theory, practical coding, and hands-on exercises.
ML Fundamentals Team
Program Directors
Aaron Gu
Program Director
Warrick Tsui
Program Director
Academics Team
Kaden Seto
Academics Team
Oscar Yasunaga
Academics Team
Andrew Magnuson
Academics Team
Jessica Chen
Academics Team
Riyad Valiyev
Academics Team
Chloe Nguyen
Academics Team
Matthew Tamura
Academics Team
Jingmin Wang
Academics Team
Thank you to IBM for sponsoring us!
