Module: Fundamental Learning Algorithms

• Probability and Calculus for ML
• Gradient descent
• Naive Bayes
• K-Nearest Neighbors
• Decision trees
• Random forests
• Support vector machines
• Unsupervised learning
• Dimensionality reduction
• Reinforcement learning

Dates: Wed, Jan 25 - Wed, Feb 8