Course Outline#
We will start from sample space, conditional probability etc, and go all the way to reading papers on GANs, VAE, Diffusion and Deepseek
Introduction to Probability
Sample Space, Probabilities, Conditional Probability, Bayes Theorem
Distributions: Gaussian, Bernoulli, Softmax
Importance of parameters of distributions
Math Behind Machine Learning
Loss function, Gradient Descent, Regularization
Entropy, KL Divergence, Maximum Likelihood
Logistic and Linear Regression
GANs, VAEs, Diffusion
Intro to Reinforcement Learning- Logistic Regression
State, action, policy
Gridworld and Q-Learning
Importance sampling, policy gradient and DeepSeek
Upcoming Cohort - July 2025#
KEY DATES
Cohort Duration: July 8th - August 14th, 2025
Live Sessions: Tuesdays & Thursdays, 9:00 PM - 11:00 PM IST
Office Hours: Saturdays & Sundays, 2:30 PM - 3:30 PM IST
WHAT YOU’LL GET
- ✓ Thorough fundamentals on the math behind GenAI
- ✓ 6 weeks of structured learning with math and code together
- ✓ Direct access to industry professionals
- ✓ Exclusive WhatsApp community with peers and AI/ML experts
📅 CALENDAR
