Contents
Year 1 – Foundations of AI and Programming
Semester
1: Introduction to AI & Python Programming
Semester
2: Data, Logic, and Introductory Machine Learning
Year 2 –
Applied & Advanced AI Systems
Semester
3: Intermediate AI & Deep Learning
Semester
4: Advanced AI, Ethics, and Capstone
Optional
Certifications & Pathways.
📘
Foundational & Introductory Texts
Proposed AI Curriculum
(Textbook
list at end)
The
structure works well for a community
college or early undergraduate program, aligns with industry
expectations, and builds strong programming,
mathematical, and ethical foundations along the way.
Year 1 – Foundations of AI and Programming
Semester 1: Introduction to AI &
Python Programming
Course Title: Introduction to Artificial Intelligence with Python
Core Objectives
·
Understand
what AI is (and is not)
·
Develop
Python programming fluency
·
Learn
computational thinking and problem-solving
·
Introduce
ethical considerations of AI
Topics
·
History
and definitions of Artificial Intelligence
·
Types
of AI: Narrow vs. General AI
·
Python
fundamentals
o Variables, data types, operators
o Control structures (if/else, loops)
o Functions and modules
·
Data
structures
o Lists, tuples, dictionaries, sets
·
Basic
file input/output
·
Introduction
to algorithms
·
Debugging
and testing
·
AI
ethics, bias, and societal impact
Tools & Libraries
·
Python
3.x
·
IDLE
/ VS Code / Jupyter Notebook
Projects
·
Rule-based
chatbot
·
Simple
decision-making system (if-then logic)
·
Mini
AI ethics reflection paper
Semester 2: Data, Logic, and
Introductory Machine Learning
Course Title: Data-Driven AI and Introductory Machine Learning
Core Objectives
·
Understand
how data drives AI systems
·
Learn
foundational machine learning concepts
·
Work
with real-world datasets
Topics
·
Data
representation and preprocessing
·
NumPy
arrays and vectorized operations
·
Pandas
for data manipulation
·
Data
visualization (Matplotlib, Seaborn)
·
Probability
basics for AI
·
Introduction
to Machine Learning
o Supervised vs. unsupervised learning
o Features and labels
·
Introductory
algorithms
o Linear regression
o k-Nearest Neighbors
o Decision trees
·
Model
evaluation basics (accuracy, precision, recall)
·
Overfitting
and underfitting
Tools & Libraries
·
NumPy
·
Pandas
·
Matplotlib / Seaborn
·
Scikit-learn
Projects
·
Predictive
model using real-world data
·
Data-driven
decision system
·
Visualization-based
insight report
Year 2 –
Applied & Advanced AI Systems
Semester 3: Intermediate AI &
Deep Learning
Course Title: Neural Networks and Applied AI
Core Objectives
·
Understand
how neural networks work
·
Apply
deep learning techniques
·
Solve
real-world AI problems
Topics
·
Review
of machine learning fundamentals
·
Mathematical
foundations (high-level)
o Vectors, matrices
o Gradients and optimization concepts
·
Neural
networks
o Perceptrons
o Activation functions
o Loss functions
·
Deep
learning architectures
o Feedforward networks
o Convolutional Neural Networks (CNNs)
·
Introduction
to Natural Language Processing (NLP)
·
Model
training and tuning
·
GPU
vs. CPU computing
·
AI
fairness and explainability
Tools & Libraries
·
TensorFlow or PyTorch
·
Keras
·
Jupyter Notebook
Projects
·
Image
classification system
·
Text
classification or sentiment analysis
·
AI
model performance report
Semester 4: Advanced AI, Ethics, and
Capstone
Course Title: Advanced AI Systems & Capstone Project
Core Objectives
·
Integrate
multiple AI techniques
·
Understand
advanced and emerging AI concepts
·
Demonstrate
mastery through a capstone project
Topics
·
Advanced
Machine Learning
o Ensemble methods
o Transfer learning
·
Advanced
NLP
o Transformers (conceptual)
o Large Language Models (LLMs – usage,
not training)
·
Reinforcement
learning (introductory)
·
AI
system deployment
o Model pipelines
o APIs and basic cloud deployment
·
Security
and privacy in AI
·
Responsible
and ethical AI development
·
AI
in industry (healthcare, finance, education, cybersecurity)
Tools & Libraries
·
Hugging
Face Transformers (introductory)
·
FastAPI or Flask
·
Git
& GitHub
·
Cloud
basics (optional)
Capstone Project
·
End-to-end
AI system
Examples:
o Intelligent tutoring system
o Predictive analytics dashboard
o AI-powered recommendation engine
o Computer vision application
·
Written
report and oral presentation
By
the end of the two-year program, students will be able to:
·
Write
clean, efficient Python code for AI applications
·
Build,
train, and evaluate machine learning models
·
Understand
and apply deep learning techniques
·
Analyze
data ethically and responsibly
·
Communicate
AI concepts clearly to technical and non-technical audiences
Optional
Certifications & Pathways
·
Python
Institute certifications
·
Google
/ IBM AI certificates
·
Transfer
pathway to a 4-year CS or AI degree
·
Entry-level
roles:
o AI Technician
o Data Analyst
o Junior Machine Learning Engineer
📘 Textbook List
📘 Foundational & Introductory Texts
🐍 Python
Programming & Foundations
·
“Automate the Boring Stuff with Python” — Al Sweigart
Great for absolute beginners to Python with practical examples in automation
(not AI-specific but ideal before diving into ML).
·
“Python Crash Course” — Eric Matthes
Solid general Python intro that builds coding confidence before AI topics.
📖 Artificial
Intelligence: A Modern Approach — Stuart Russell & Peter Norvig
Often
considered the standard AI textbook worldwide for foundational AI,
search, logic, planning, reasoning, and basic learning concepts (with pseudo-code
and conceptual depth).
📊 Machine
Learning (ML) – Core Curriculum
🧠 Hands-On
Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron
One
of the most recommended practical ML books for Python learners: it
blends core algorithms with hands-on exercises using Scikit-Learn
and deep learning libraries. Ideal for Semester 2 and 3.
🧠 Introduction
to Machine Learning with Python — Andreas Müller & Sarah Guido
Excellent
Python-focused beginner ML text centered on Scikit-Learn
with clear examples and pipelines. A great intro before deep
learning.
🐍 Python
Machine Learning — Sebastian Raschka & Vahid Mirjalili
Balances
theory and real-world implementation of ML and deep learning in Python; often
suggested for intermediate learners.
🔍 Deep
Learning (DL)
🧠 Deep
Learning — Ian Goodfellow, Yoshua Bengio & Aaron Courville
Considered
the definitive deep learning textbook, covering neural network theory
from basics to advanced topics. Excellent for advanced understanding.
🧠 Deep
Learning with Python — François Chollet
A
more hands-on, code-driven introduction to deep learning using the Keras API in TensorFlow — great
for Semester 3 deep learning
projects.
📘 Deep
Reinforcement Learning Hands-On — Maxim Lapan
Practical
introduction to RL with Python and PyTorch projects —
perfect for later in Semester 4
or advanced electives.
(For
deeper theoretical RL:)
·
“Reinforcement Learning: An Introduction” — Sutton & Barto
A classic RL theory text (not Python code, but foundational).
🗣️ Natural
Language Processing (NLP)
📖 Natural
Language Processing with Python — Steven Bird, Ewan Klein & Edward Loper
Covers
core NLP tasks and Python techniques using libraries like NLTK — excellent for
NLP units.
📖 Books on
NLP with Transformers
Titles
such as Natural Language Processing with Transformers provide modern deep learning-based NLP coverage,
including Python usage for transformer models.
📈 Advanced
& Specialized Reads
📊 Pattern
Recognition and Machine Learning — Christopher Bishop
Great
for mathematical foundations of ML and probabilistic models — more
advanced/theoretical study.
📖 The Elements of Statistical Learning
A
classic for deep statistical insights into ML (more mathematical, stronger
theory).