TMLS2019

Overview

Explore advanced techniques in machine learning and data science with insights from industry experts at the Toronto Machine Learning Series conference.

Syllabus

• Introduction to Advanced Machine Learning Techniques
Overview of the Machine Learning Landscape
Key Concepts in Machine Learning
Introduction to the Toronto Machine Learning Series
• Deep Learning and Neural Networks
Advanced Architectures: CNNs, RNNs, and Transformers
Training Deep Neural Networks: Best Practices and Challenges
Case Studies from Industry Experts
• Natural Language Processing (NLP)
Recent Advances in NLP
Applications of NLP in Industry
Hands-on Workshop: Building an NLP Model
• Reinforcement Learning
Principles of Reinforcement Learning
Applications in Robotics and Autonomous Systems
Interactive Simulation Exercises
• Data Science Techniques
Exploratory Data Analysis (EDA) for Machine Learning
Feature Engineering and Data Preprocessing
Case Studies: Insights from Industry Data Scientists
• Machine Learning in Production
Deployment and Scalability of Machine Learning Models
Tools and Frameworks for MLOps
Real-world Challenges and Solutions
• Ethical and Responsible AI
Understanding Bias and Fairness in AI
Strategies for Ensuring Ethical AI Deployment
Panel Discussion: Perspectives from Industry Leaders
• Closing and Future Directions in AI
Latest Trends and Research in AI
Expert Talks on Future Directions
Networking and Collaborative Opportunities
• Final Project and Assessment
Team-Based Project: Solving a Real-World Problem using ML
Presentation and Peer Review
Feedback and Course Reflections

Subjects

Conference Talks