ApplyThis course will provide you with the fundamental understanding of the concepts behind Generative Models and Reinforcement Learning and apply them to real world problems.
Split into 2 parts, the course starts with generative models which cover autoencoders, variational autoencoders and Generative Adversarial Networks. The second half of this course will introduce you to the field of Reinforcement Learning. At the end of the course, you will be able to build efficient generative models, and work with reinforcement learning problems.
This course will prepare you for elite, much sought after AI positions. It will also equip you to conduct original research in your area of interest.
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After successfully completing the program, you will be:
After successfully completing the program, you will be:
Learners and practitioners who have an understanding of intermediate AI concepts including Language Modeling and Transfer Models and are looking to master more advanced concepts. You should have strong foundations in statistics, computer science & mathematics.
Knowledge of beginning and intermediate AI as exemplified by the topics in AI: Basics
Able to build complex language models and gauge their performance, as covered in AI 3
Prior knowledge of high level machine learning libraries such as keras
Bi-weekly lessons with labs and quizzes
Ten or more hours/week to interact with an experienced & accomplished mentor
Complex problems that challenge you to apply what you learned
10–12 week long Capstone Project with one of our partner companies or faculty
GANs
Markov Decision Processes
Deep Q Networks
Policy Gradient Methods
Actor Critic models
Model based methods
Project Week
Our programs are priced for access, and are affordable for most around the world who seek the best training in sought after areas like AI and Data Science. For those who might still need financing, we have EMI payments available.
A small number of Univ.AI Scholarships are available for the best candidates. Learn More
