> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # AI Course Syllabus > Syllabus for Introduction to Artificial Intelligence course. ## Books 1. **AIMA** - [Artificial Intelligence: A Modern Approach](https://www.amazon.com/Artificial-Intelligence-A-Modern-Approach/dp/0134610997) by Stuart Russell, 4th edition, 2021. Also available at [aima.cs.berkeley.edu](http://aima.cs.berkeley.edu/). This book is required. 2. **GERON** - [Hands-On Machine Learning with Scikit-Learn and PyTorch](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/), Oct 2025, Free for NJIT and NYU students. Very useful for those new to numerical Python and Pytorch. For students that have no access the open-source [Dive into Deep Learning](https://d2l.ai/) book is also a good choice. 3. **DL** - [Deep Learning](https://www.deeplearningbook.org/). This book provides the necessary depth for statistical learning concepts in this course. ## Planned Schedule ### Part I: 2D Perception and Machine Learning | Lecture | Topic | Description | | ------- | -------------------- | --------------------------------------------------------------------------------------------------------------------- | | **1** | Introduction to AI | Systems approach to AI. Overview of AI agents and course roadmap. **Reading:** AIMA Chapters 1 & 2 | | **2** | Supervised Learning | Perception subsystem, reflexive agents, classification and regression with classical ML. **Reading:** AIMA Chapter 19 | | **3** | Deep Neural Networks | From Perceptron to MLPs, SGD optimization, backpropagation fundamentals. **Reading:** AIMA Chapter 21, DL Chapter 6 | | **4** | CNNs | Convolutional Neural Networks architecture and applications. **Reading:** DL Chapters 9 & 10, AIMA Chapter 25 | | **5** | Scene Understanding | Object Detection, Semantic and Instance Segmentation. **Reading:** AIMA Chapter 25 | | **6** | Probabilistic Models | Recursive state estimation, Dynamic Bayesian Networks, Kalman filters. **Reading:** AIMA Chapters 12, 13 & 14 | ### Part II: Natural Language Processing | Lecture | Topic | Description | | ------- | ----------------- | ---------------------------------------------------------------------------------------------------------------- | | **7** | NLP Fundamentals | Problem formulation and component mechanics. **Reading:** AIMA Chapter 23 | | **8** | Language Modeling | RNN/LSTM architectures, attention mechanisms, Transformer framework. **Reading:** AIMA Chapter 24, DL Chapter 10 | ### Part III: Reasoning and Planning without Interactions | Lecture | Topic | Description | | ------- | -------------------------- | ----------------------------------------------------------------------------------------------------------------- | | **9** | Knowledge Representation | Symbolic AI, propositional logic, theorem proving, Knowledge Base construction. **Reading:** AIMA Chapter 7 | | **10** | Problem Solving and Search | Planning, A\* algorithm, handling deterministic and stochastic environments. **Reading:** AIMA Chapters 3, 4 & 11 | ### Part IV: Planning with Interactions - Reinforcement Learning | Lecture | Topic | Description | | ------- | ---------------------- | -------------------------------------------------------------------------------------------------- | | **11** | MDPs and POMDPs | Sequential decision making, reward signals, policy computation. **Reading:** AIMA Chapters 16 & 17 | | **12** | Reinforcement Learning | Bellman equations, policy iteration, REINFORCE algorithm with DNNs. **Reading:** AIMA Chapter 22 | | **13** | Review | Final lecture before the exam | *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/courses/ai/syllabus/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).