At a Glance

Name: UTMIST - Machine Learning Fundamentals
Price: Free CAD
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Author: utmist_academics

The Machine Learning Fundamentals (MLF) Program is a beginner-friendly initiative offering foundational technical and theoretical knowledge in ML/AI. Through structured workshops covering topics like linear regression and neural networks, and a mentored group project, you will build practical skills and gain the confidence needed to start your journey in this field. It's designed to equip you with a solid understanding of core concepts, preparing you for future ML/AI endeavors.

Embark on your Machine Learning (ML) and Artificial Intelligence (AI) journey with the Machine Learning Fundamentals (MLF) Program! This course is your essential starting block for navigating the exciting, high-demand field of ML/AI.

Why is this course important? The power of machine learning is transforming every industry, from tech and finance to healthcare and entertainment. Having a solid, fundamental understanding of ML concepts is no longer just a specialization—it's becoming a crucial skill for innovators of tomorrow. This program is important because it provides a beginner-friendly, guided path through the foundational technical and theoretical knowledge that most self-learners struggle to piece together.

By taking this course, you will gain a kickstart and the confidence to explore this field on your own. You'll gain a solid understanding of core concepts, build practical skills through workshops and a guided project, and have access to valuable mentorship from the University of Toronto Machine Intelligence Student Team (UTMIST) academics. Upon completion, you won't just know about ML; you'll be equipped to tackle your own ML/AI initiatives and have a clear vision for your next steps in this domain.

Course Syllabus

Upon completing this program, you will be able to:

Define and explain the core concepts of foundational machine learning, including linear and logistic regression.
Implement and interpret fundamental algorithms like Gradient Descent and various loss functions.
Describe the architecture of basic neural networks (Multi-Layer Perceptrons) and the role of activation functions.
Apply key concepts of the ML Development Cycle, such as identifying and mitigating issues like overfitting, underfitting, and managing bias/variance trade-offs.
Analyze practical problems and apply specialized ML techniques like K-means clustering and Decision Trees.
Collaborate on a group project, applying your learned skills in a structured, professional environment.

Module 1: Foundational ML Concepts

Topic 1: Introduction to Basic Concepts for ML (Linear Regression, Logistic Regression, Loss Functions, Gradient Descent) |
Topic 2: The ML Development Cycle (Overfitting/Underfitting, Bias/Variance)

Module 2: Introduction to Neural Networks and Specialized Topics
Topic 1: Intro to Neural Networks (Activation Functions, Multi-Layer Perceptrons (MLP), Neural Network Implementation (e.g., in DL libraries or NumPy, Backpropagation)
Topic 2: "Special" Topics (Decision Trees, Unsupervised Learning - Anomaly Detection, K-means Clustering, Recommender Systems - Collaborative Filtering, Content-Based Filtering)

General Information

This program is designed to be completed over a single semester, with a recommended timeline of approximately 7 to 8 weeks of focused workshops and learning, followed by the guided group project phase. The learning experience is highly engaging, utilizing technical workshops/lectures for concept delivery.

Assessment Style: Your progress and learning will be evaluated through the successful completion and presentation of a guided group project. This project is structured similarly to UTMIST's own initiatives, giving you valuable real-world experience. Mentors will provide continuous support and feedback throughout the learning process and project development.

Recommended Skills Prior to Taking this Course

This program is specifically designed for Beginners—individuals who are new to the field of ML/AI or are seeking mentorship to develop foundational skills.

Programming Language: A basic familiarity with Python is highly recommended, as it is the primary language used in the ML field.
Mathematics: A general understanding of high-school level algebra is helpful for grasping concepts like linear functions and gradients.
Technology: No advanced technology setup is required. The program will leverage environments that often have necessary tools (like Docker) pre-installed. The learning platform works best with current versions of modern web browsers, including Chrome, Edge, Firefox, or Safari.

Offered By: IND

UTMIST - Machine Learning Fundamentals

At a Glance

Course Syllabus

General Information