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AdvancedAI & MLLive Data

AI-2.5: Modern Artificial Intelligence

Master advanced AI paradigms through reinforcement learning and generative AI. Build intelligent agents, train game-playing AI, create generative mode...

AdvancedAI & MLLive Data

AI-2.5: Modern Artificial Intelligence

Master advanced AI paradigms through reinforcement learning and generative AI. Build intelligent agents, train game-playing AI, create generative mode...

Lessons

18

Activities

18

Projects

7

Duration

18 sessions • 6 months

About This Course

Master advanced AI paradigms through reinforcement learning and generative AI. Build intelligent agents, train game-playing AI, create generative models, and understand how RL and GenAI converge through RLHF.

Prerequisites

**Required**: AI-2 completion (or equivalent ML/CV background)

This course consists of 18 structured sessions over 6 months. Each session includes lessons, activities, projects, and assessments to ensure comprehensive learning.

1

Introduction to Reinforcement Learning

Agent-environment interaction, MDP basics

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Course Information

Course CodeAI25

Age Group10+ years old

Class SizeMax 8 students

Teacher Ratio1:8

Learning ModeHYBRID

Last Updated12/5/2025

Data SourceGitHub

Key Benefits

AI
Master AI through practical application

Python proficiency with PyTorch basics

Understanding of neural networks and optimization

Google account for Google Colab (Pro recommended)

Skills You'll Learn

AI

2

Q-Learning and Value Functions

Tabular Q-Learning, epsilon-greedy exploration

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3

Deep Q-Networks (DQN)

Function approximation, experience replay

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4

Policy Gradient Methods

Policy-based RL, REINFORCE algorithm

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5

Actor-Critic Methods

Actor-critic architecture, advantage estimation

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6

Proximal Policy Optimization (PPO)

Trust region methods, PPO clipping

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7

Multi-Armed Bandits and Exploration

Bandit problems, UCB, contextual bandits

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8

RL in Practice - Debugging and Deployment

Debug RL failures, reward shaping

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9

Introduction to Generative Models

Generative vs discriminative, latent spaces

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10

Variational Autoencoders (VAEs)

Encoder-decoder, reparameterization trick

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11

Generative Adversarial Networks (GANs)

Adversarial training, minimax objective

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12

Advanced GAN Architectures

StyleGAN features, conditional GANs, WGAN-GP

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13

Diffusion Models

Denoising diffusion, U-Net architecture

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14

Transformer Architectures for Generation

Self-attention, autoregressive generation

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15

Large Language Models (LLMs) Fundamentals

LLM architecture, prompt engineering

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16

Reinforcement Learning from Human Feedback (RLHF)

3-stage RLHF pipeline, reward model training

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17

Multi-Modal AI - Vision and Language

Cross-modal learning, CLIP, text-to-image

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18

The Future of AI - Integration and Ethics

RL + GenAI integration, AI safety

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Learning Outcomes

Practical Skills
Build real-world applications and projects
Problem Solving
Develop computational thinking and problem-solving abilities

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