Artificial Intelligence (AI)

Artificial Intelligence (AI)

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Duration

3 Days

Certified Instructor

Course Id

nextec1200

Course Description

Course Overview

The Artificial Intelligence (AI) course provides an in-depth understanding of the fundamental concepts and techniques of AI, enabling students to develop intelligent systems and applications. The course explores various AI methodologies, including machine learning, natural language processing, computer vision, and knowledge representation. This program offers a thorough exploration of the technical aspects of the subject, allowing students ample opportunities to delve into its intricacies. Students will gain hands-on experience with AI tools and frameworks, allowing them to design and implement intelligent solutions for real-world problems. Through this program, students and enthusiasts of Artificial Intelligence gain the right knowledge and guidance needed to excel in the field. 

Prerequisites

  • Basic programming skills (preferably in Python)
  • Familiarity with data structures and algorithms
  • Understanding of probability and statistics
  • Knowledge of linear algebra and calculus
  • Some exposure to computer science concepts and algorithms

Methodology

The course adopts a blended learning approach, combining lectures, practical exercises, coding assignments, and hands-on labs. The lectures cover the theoretical foundations of AI, while the practical exercises and coding assignments provide opportunities to apply the learned concepts. The labs focus on implementing AI algorithms, working with datasets, and developing AI applications using popular frameworks.

Course Outline

Introduction to Artificial Intelligence

Historical overview of AI

AI applications and impact on society

Intelligent agents and problem-solving

Search Algorithms

Uninformed search: breadth-first search, depth-first search

Informed search: A* search, heuristic functions

Adversarial search: minimax algorithm, alpha-beta pruning

Knowledge Representation and Reasoning

Propositional logic and predicate logic

Inference rules and resolution

Semantic networks and frames

Machine Learning Fundamentals

Supervised learning: classification and regression

Unsupervised learning: clustering and dimensionality reduction

Evaluation metrics and model selection

Neural Networks and Deep Learning

Artificial neurons and activation functions

Feedforward neural networks and backpropagation

Convolutional neural networks and recurrent neural networks

Natural Language Processing

Text preprocessing and feature extraction

Language modeling and text classification

Sequence-to-sequence models and machine translation

Computer Vision

Image processing techniques

Feature extraction and object recognition

Convolutional neural networks for image classification

Reinforcement Learning

Markov decision processes

Value iteration and policy iteration

Q-learning and deep Q-networks

Outcome

 Upon completing the course, students will:

  • Understand the fundamental concepts, methodologies, and techniques of AI.
  • Be able to design and implement intelligent systems using machine learning, natural language processing, computer vision, and reinforcement learning.
  • Gain proficiency in popular AI frameworks and tools.
  • Apply AI algorithms to solve real-world problems and improve decision-making processes.
  • Analyze and evaluate the performance of AI models and systems.

Labs 

The course includes hands-on labs to reinforce theoretical concepts and provide practical experience. The labs may include:

  • Implementing search algorithms (e.g., breadth-first search, A* search) for solving navigation problems.
  • Building a knowledge-based system using propositional and predicate logic for expert reasoning.
  • Developing a machine learning pipeline for a classification task using popular libraries (e.g., sci-kit-learn, TensorFlow).
  • Training a convolutional neural network for image classification on a dataset (e.g., CIFAR-10).
  • Building a natural language processing application (e.g., sentiment analysis, chatbot) using recurrent neural networks and language modeling.
  • Implementing a reinforcement learning agent to play a game or solve a control problem.

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