Artificial Intelligence

This course provides a comprehensive introduction to Artificial Intelligence (AI), its fundamental concepts, and the various techniques used to develop AI systems. As AI continues to transform industries, from healthcare and finance to transportation and entertainment, this course equips students with the knowledge and practical skills to understand, design, and implement AI solutions. The course covers essential topics such as machine learning, natural language processing, computer vision, and robotics, while also addressing ethical considerations and realworld applications of AI.

By the end of the course, students will have a foundational understanding of AI algorithms and how they are applied in different fields, along with handson experience using key AI tools and frameworks.

 Course Objectives:

1. Understand AI Fundamentals: Learn the history of AI, key concepts, and core components like machine learning, neural networks, and natural language processing.
   2. Develop Practical AI Solutions: Gain handson experience with building and deploying AI models, including classification, regression, clustering, and deep learning.
   3. Explore Machine Learning (ML): Study various ML algorithms, including supervised, unsupervised, and reinforcement learning.
   4. Examine AI in RealWorld Applications: Understand how AI is used in industries like healthcare, finance, entertainment, and autonomous systems.
   5. Address Ethical Issues in AI: Explore the ethical implications of AI, including bias, fairness, transparency, and the societal impact of automation.

 Course Modules:

1. Introduction to Artificial Intelligence

What is AI?

History and evolution of AI
Types of AI: Narrow AI vs. General AI
Key applications: Healthcare, autonomous driving, finance, gaming
Foundational Concepts of AI

Intelligent agents and environments
Problemsolving and search algorithms (e.g., A Search, BreadthFirst Search)
AI Problem Solving Techniques

Logical reasoning and rulebased systems
Knowledge representation: Semantics, frames, ontologies

2. Machine Learning (ML) Basics

Supervised Learning

Understanding regression and classification problems
Algorithms: Linear regression, logistic regression, decision trees, kNN (kNearest Neighbors)
Evaluating models: Accuracy, precision, recall, F1 score
Unsupervised Learning

Clustering techniques: Kmeans, hierarchical clustering, DBSCAN
Dimensionality reduction: PCA (Principal Component Analysis)
Anomaly detection
Reinforcement Learning

Understanding agents, actions, rewards, and environments
QLearning and deep Qnetworks (DQN)
Applications: Robotics, game playing (e.g., AlphaGo)

3. Deep Learning and Neural Networks

Introduction to Neural Networks

Artificial neural networks (ANNs): Structure and functioning
Activation functions (e.g., ReLU, Sigmoid, Tanh)
Backpropagation and gradient descent
Convolutional Neural Networks (CNNs)

How CNNs work: Convolutional layers, pooling layers, and fully connected layers
Applications in image recognition, video processing, and computer vision
Recurrent Neural Networks (RNNs) and LSTMs

Time series data, sequential models
Long ShortTerm Memory (LSTM) networks for handling vanishing gradients
Applications in speech recognition and natural language processing

4. Natural Language Processing (NLP)

Text Processing Basics

Tokenization, stop words, stemming, lemmatization
BagofWords and TFIDF (Term FrequencyInverse Document Frequency)
Text Classification and Sentiment Analysis

Using supervised learning for classifying text
Sentiment analysis with machine learning models
Advanced NLP Techniques

Word embeddings (Word2Vec, GloVe)
Transformer models: BERT, GPT
Language translation and chatbots (e.g., Google Translate, Siri)

5. Computer Vision

Image Processing Basics

Image representation, pixel manipulation
Edge detection, filters, and image enhancement
Object Detection and Recognition

Techniques: YOLO (You Only Look Once), RCNN
Face recognition, object tracking
Convolutional Neural Networks (CNNs) in Computer Vision

Implementing CNNs for image classification, detection, and segmentation
Applications: Autonomous vehicles, medical imaging, security surveillance

6. AI in RealWorld Applications

Healthcare and AI

AI in diagnostics, medical imaging, drug discovery, and personalized medicine
Challenges and ethical issues in AI for healthcare
Finance and AI

AI applications in fraud detection, algorithmic trading, and credit scoring
Predictive analytics and risk management
Autonomous Systems and Robotics

Autonomous vehicles and drones
Robotics: Industrial robots, AI in manufacturing, and service robots
AI in Gaming and Entertainment

AI in game development, NPCs (NonPlayer Characters), and adaptive gameplay
Content recommendation systems (e.g., Netflix, YouTube)

7. Ethics, Bias, and Future of AI

Ethical Considerations in AI

Privacy, transparency, and accountability
Autonomous decisionmaking and AI bias
AI Bias and Fairness

Understanding and mitigating bias in AI models
Fairness metrics and fairnessaware algorithms
The Future of AI

The rise of AGI (Artificial General Intelligence)
The potential impact of AI on jobs and society

 Practical Skills and Labs:

 Handson Exercises:

Implementing simple machine learning algorithms using Python libraries such as Scikitlearn, TensorFlow, and PyTorch
Building and training neural networks for image classification and text analysis
Working with real datasets (e.g., MNIST for digit recognition, IMDB for sentiment analysis)
AI Projects:

Sentiment analysis on social media data
Object detection in images using pretrained models
Building a simple chatbot using NLP techniques

Assessment Methods:

 Quizzes and Exams:

Testing theoretical knowledge and understanding of key AI concepts, algorithms, and models.
Handson Lab Assignments:

Practical tasks like building, training, and evaluating machine learning models, and experimenting with deep learning techniques.
Capstone Project:

Students work on a final project where they apply AI techniques to solve a realworld problem (e.g., image classification, chatbots, recommendation systems).

 Prerequisites:

Basic knowledge of programming (preferably Python) and data structures
Understanding of high schoollevel mathematics, especially algebra and probability
No prior experience in AI or machine learning is required

 Career Pathways After the Course:

This course is an excellent foundation for students who want to pursue careers in the rapidly growing field of artificial intelligence. Career options include:

AI/Machine Learning Engineer
Data Scientist
Deep Learning Researcher
Computer Vision Engineer
NLP Engineer
Robotics Engineer
AI Ethics Specialist