Machine Learning II: Deep Learning and Applications
Info
- Instructor: Jian Tang
- Trimester: Winter 2020
- When:
- Class 1: Thu: 8:30 - 11:30 am
- Class 2: Fri: 3:30 - 6:30 pm
- Where:
- Quebecor, Côte-Sainte-Catherine
- Class 2 on 3 / 20: BDC, Côte-Sainte-Catherine
- Office hour: Thu: 2:00 - 3:00 pm, 4.805, HEC Montréal
Course Objectives
- Understand machine learning basics
- Understand deep learning basics such as feedforward neural networks, convolutional neural networks, and recurrent neural networks
- Know several advanced topics in deep learning, including applications in natural language understanding, graph representation learning, recommender systems, and deep generative models
- Learn to use PyTorch for applying deep learning techniques to solve real-world problems
Prerequisites
- Linear algebra
- Python programming language
- One of following courses
- Machine Learning I: Large-scale machine learning and decision making
- Data Mining
Evaluation
Due to the pandemic, the final exam and the poster presentation are cancelled.
- Homework:
20%60% = 3 * 20% - Presentations: 10%
- Projects: 30% = proposal 5% +
poster 10%+ report 25% Final Exam: 30%
