Course Description: 

This course provides an introduction to the field of Artificial Intelligence, covering foundational concepts, algorithms, and practical applications. Students will gain a broad understanding of AI techniques and their real-world applications, with a focus on machine learning, search algorithms, and knowledge representation.

Prerequisites:

• Basic programming knowledge (preferably in Python)

• Introductory courses in mathematics (linear algebra, probability, and statistics)

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Module 1: Introduction to AI

• Overview of AI: Definition, history, and applications

• Types of AI: Narrow AI vs. General AI

• AI vs. Machine Learning vs. Deep Learning

• Applications of AI: Robotics, NLP, image recognition, recommendation systems, etc.

Module 2: Problem Solving and Search Algorithms

• Problem Solving in AI: Definition of state space and problem formulation

• Search Algorithms:

o Uninformed Search (Breadth-first search, Depth-first search)

o Informed Search (A* algorithm, Heuristic search)

o Local Search (Hill climbing, Simulated Annealing)

• State Space Representation: Trees and graphs

Module 3: Knowledge Representation and Reasoning

• Knowledge Representation: Propositional logic, first-order logic

• Inference: Forward and backward chaining, resolution

• Reasoning under uncertainty: Probabilistic reasoning, Bayes' Theorem

Module 4: Machine Learning Overview

• Introduction to Machine Learning: Types of learning (supervised, unsupervised, reinforcement)

• Supervised Learning:

o Linear regression, classification algorithms (e.g., Decision Trees, K-Nearest Neighbors)

o Overfitting and underfitting

o Evaluation metrics (accuracy, precision, recall, F1 score)

• Unsupervised Learning:

o Clustering algorithms (K-means, hierarchical clustering)

o Dimensionality reduction (PCA)

Module 5: Neural Networks and Deep Learning

• Introduction to Neural Networks: Perceptron, Activation functions

• Deep Learning: Multi-layer perceptron (MLP), Backpropagation

• Convolutional Neural Networks (CNNs): Applications in image processing

• Recurrent Neural Networks (RNNs): Applications in time series and NLP

Module 6: Natural Language Processing (NLP)

• Introduction to NLP: Text processing, tokenization

• Basic NLP Tasks: Text classification, sentiment analysis

• Advanced NLP: Named Entity Recognition (NER), part-of-speech tagging

• Transformers and BERT: Modern NLP techniques

Module 7: Reinforcement Learning

• Overview of Reinforcement Learning: Key concepts (agent, environment, reward)

• Markov Decision Process (MDP)

• Q-learning: Basic algorithms and applications

• Policy Gradients and Deep Q Networks (DQN)

Module 8: Ethics and Future of AI

• Ethical Considerations in AI: Bias in algorithms, fairness, privacy concerns

• AI Governance and Regulation

• AI in Society: Job automation, AI in healthcare, education, and more

• Future Trends in AI: AI and AGI (Artificial General Intelligence)

Module 9: AI Applications and Case Studies

• AI in Robotics: Autonomous systems, sensors, and control

• AI in Healthcare: Diagnostic tools, predictive models

• AI in Business: Recommendation systems, customer service bots

• AI in Games: AlphaGo, game-playing agents

Module 10: Final Project and Review

• Project: Students will work on an AI project applying concepts learned in the course (e.g., building a simple ML model, developing a chatbot, or solving a problem using search algorithms).

• Review: Recap of key topics covered in the course

Learning Outcomes:

By the end of the course, students should be able to:

• Understand key concepts in AI and machine learning.

• Implement basic AI algorithms (search algorithms, machine learning models).

• Apply AI techniques to solve real-world problems.

• Understand the ethical implications of AI technology.