Admissions Open for JANUARY Batch
Master advanced topics like information theory, convex optimization, and tensor calculus.
Days : Tue & Thu
Duration : 10 Hours
Timings: 8 - 10 PM IST
Try Risk-free, 15 Days Money Back Guarantee
1 Months
5 Hours
Tue & Thu
Advanced Maths
Master advanced topics – information theory, convex optimization, and tensor calculus.
Online Live Instructor-Led Learning
10 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Entropy, KL divergence, and uncertainty math
Convex optimization techniques
Get familier with
Stochastic processes and Markov models
Variational inference and EM algorithms
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners seeking to master advanced mathematical concepts like optimization, vector calculus, and probability theory, vital for research and developing sophisticated AI solutions
Prerequisites
Deep learning math and strong calculus, statistics foundation.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Deep learning math and strong calculus, statistics foundation.
Who Should Enroll?
This course is for learners seeking to master advanced mathematical concepts like optimization, vector calculus, and probability theory, vital for research and developing sophisticated AI solution
By end of this course
Get Stronger in
- Entropy, KL divergence, and uncertainty math
- Convex optimization techniques
Get Familiar in
- Stochastic processes and Markov models
- Variational inference and EM algorithms
Course Contents
What is covered: Measuring information, uncertainty (entropy, KL divergence).
Application: Loss functions, generative models, attention mechanisms.
Example:
1. Next Generation: Using entropy to measure how unpredictable a model’s output is.
2. KL Divergence: Comparing how close a model’s predictions are to actual data.
What is covered: Finding best solutions under constraints (convex optimization).
Application: Designing new algorithms, improving model training.
Example:
1. Support Vector Machines (SVM): Using convex optimization to find the best separating line.
2. Resource Allocation: Optimizing use of memory and compute in large models.
What is covered: Systems that evolve with randomness (Markovchains).
Application: Time series, reinforcement learning, probabilistic modeling.
Example:
1. Weather Prediction: Using Markov chains to model changes in weather over time.
2. Game AI: Modeling possible moves and outcomes in chess.
What is covered: Estimating complex probabilities, building generative models.
Application: Variational autoencoders (VAEs), probabilistic ML.
Example:
1. Adam Optimizer: Used in training large models like GPT for faster and stable learning.
2. Learning Rate Scheduling: Adjusting how quickly a model learns over time.
What is covered: Calculus for functions with many variables, tensors.
Application: Deep learning, high-dimensional data modeling.
Example:
1. Neural Network Layers: Calculating gradients for layers with many inputs/outputs.
2. Physics Simulations: Modeling complex systems in AI research.
What is covered: Measuring information, uncertainty (entropy, KL divergence).
Application: Loss functions, generative models, attention mechanisms.
Example:
1. Next Generation: Using entropy to measure how unpredictable a model’s output is.
2. KL Divergence: Comparing how close a model’s predictions are to actual data.
What is covered: Finding best solutions under constraints (convex optimization).
Application: Designing new algorithms, improving model training.
Example:
1. Support Vector Machines (SVM): Using convex optimization to find the best separating line.
2. Resource Allocation: Optimizing use of memory and compute in large models.
What is covered: Systems that evolve with randomness (Markovchains).
Application: Time series, reinforcement learning, probabilistic modeling.
Example:
1. Weather Prediction: Using Markov chains to model changes in weather over time.
2. Game AI: Modeling possible moves and outcomes in chess.
What is covered: Estimating complex probabilities, building generative models.
Application: Variational autoencoders (VAEs), probabilistic ML.
Example:
1. Adam Optimizer: Used in training large models like GPT for faster and stable learning.
2. Learning Rate Scheduling: Adjusting how quickly a model learns over time.
What is covered: Calculus for functions with many variables, tensors.
Application: Deep learning, high-dimensional data modeling.
Example:
1. Neural Network Layers: Calculating gradients for layers with many inputs/outputs.
2. Physics Simulations: Modeling complex systems in AI research.