CSL-537: Deep Learning

Pre-requisite

1. Make sure you are fundamental understanding in Linear Algebra and Probability. Below are reference material to revise the basics

2. Python Programming

3. Foundational course in AI or ML

Loss functions: Mean Square Error, Negative Log Likelihood, Cross Entropy

Optimization techniques: Gradient Descent, Stochastic Gradient Descent, Constrained convex optimization (Lagrangians)

Supervised Techniques: Linear Regression, Logistic Regression, Perceptron Classification, Decision Trees, Random Forests, SVMs, Naive Bayes, k-NN

Unsupervised Techniques: K-means Clustering, Gaussian Mixture Modeling (GMMs), Expectation-Maximization (EM), Principal Component Analysis (PCA)

4. Chapters 1-5 of book Deep Learning by Ian Goodfellow:

Discussions

Microsoft Teams

Course Content

To be updated ...

Introduction

Multi-layered Perceptrons

Backpropagation

Regularization: L1-L2 Norms

Dropouts

Optimization: Challenges. Stochastic Gradient Descent

Advanced Optimization Algorithms

Batch and Layer Normalization

Convolutional Networks (CNNs)

...

Book References

Deep Learning by Ian Goodfellow

Paper References

To be updated ...

Grading Scheme (tentative)

CWS: 30%

MTE: 30%

ETE: 40%