AI Course Syllabus

Books

AIMA - Artificial Intelligence: A Modern Approach by Stuart Russell, 4th edition, 2021. Also available at aima.cs.berkeley.edu. This book is required.

GERON - Hands-On Machine Learning with Scikit-Learn and PyTorch, 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 book is also a good choice.

DL - Deep Learning. This book provides the necessary depth for statistical learning concepts in this course.

Some of the assigned reading sits on the O’Reilly platform, which your university provides at no cost. See O’Reilly access if you have not signed in yet.

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

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​Books

​Planned Schedule

​Part I: 2D Perception and Machine Learning

​Part II: Natural Language Processing

​Part III: Reasoning and Planning without Interactions

​Part IV: Planning with Interactions - Reinforcement Learning

Books

Planned Schedule

Part I: 2D Perception and Machine Learning

Part II: Natural Language Processing

Part III: Reasoning and Planning without Interactions

Part IV: Planning with Interactions - Reinforcement Learning