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.

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

​Books