| 1 |
Intro to ML |
Ryan |
Fundamentals, Types, and Applications of ML |
| 2 |
Supervised Learning |
Aadam |
Supervised Learning, Linear Regression, Single Perceptron |
| 4 |
Math of ML |
Aadam |
Linear Algebra, Calculus, Backpropagation, Gradient Optimizers |
| 3 |
Advanced Regression |
Suryam |
Multi-Layer Perceptrons, Nonlinear Regression, Activation Functions |
| 5 |
Python for ML |
Karthik & Ruchira |
NumPy, Pandas, PyTorch, Hugging Face |
| 6 |
Computer Vision |
Karthik & Kien |
ConvNet, Classification Loss, Other Neural Network (NN) Models |
| 7 |
Generative Machine Learning |
Suryam & Kien |
Variational Autoencoders, Generative Adversarial Networks |
| 8 |
Reinforcement Learning |
Aadam |
Q-Learning, Tabular Reinforcement, Function Approximation |
| 9 |
Deep Reinforcement Learning |
Ryan |
Deep-Q Network, Proximal Policy Optimization |
| 10 |
Natural Language Processing (NLP) |
Ruchira |
Basic NLP, Recurrent NNs (RNNs), Long Short-Term Memory (LSTM) |
| 11 |
Evolutionary Computation |
Ryan |
NeuroEvolution of Augmenting Topologies (NEAT), HyperNEAT, Covariance Matrix Adaptation Evolution Strategy |
| 12 |
Information Retrieval and Recommender Systems |
Kim |
Information Retrieval, Recommender Systems |
| 13 |
Real-World Applications |
All Lectueres |
Healthcare, Finance, Image Recognition, Ethics, Project Showcase, Networking |