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.
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