Artificial Intelligence

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.


Instruktur
Jadwal Training
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