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Introduction to Artificial Intelligence

This courseware is designed to guide you through the fundamentals of AI in a clear and approachable way. You will explore what AI truly is, how it works, and what opportunities and challenges lie ahead—all without needing a background in advanced mathematics.

Rrreinaldo

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Core Programming AI1 mins

Algorithm and Data Structure

Algorithm and Data Structure course introduces students to the fundamental structures and algorithmic techniques that power modern artificial intelligence systems. Through a practice-centered approach, students learn to analyze algorithm efficiency, implement core data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs, and understand how these structures enable scalable and efficient AI applications. Each topic is connected to real machine learning and data processing scenarios such as batch handling, vocabulary indexing, decision trees, top-k retrieval, and graph-based recommendations. By combining conceptual foundations, hands-on coding, PCL mini-projects, and AI-driven problem solving, the course equips students with essential algorithmic thinking and prepares them for advanced AI coursework, engineering roles, and technical interviews.

Introduction to Large Language Models

Introduction to Large Language Models is an advanced artificial intelligence course that explores the architecture, training, and applications of modern language models that have revolutionized natural language processing. The course covers foundational concepts including transformer architectures, attention mechanisms, tokenization strategies, and scaling laws, alongside practical techniques like prompt engineering and parameter-efficient fine-tuning (such as LoRA). Through hands-on experiments using frameworks like Hugging Face Transformers and OpenAI APIs, students will build and evaluate practical applications such as chatbots, code generation tools, and text summarization systems. By the end of the course, students will be equipped to leverage foundation models for complex reasoning and generation tasks while understanding the technical challenges and responsible deployment of large-scale AI systems.

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning is a specialized artificial intelligence course that introduces students to the principles and algorithms of learning through interaction with dynamic environments. The course covers foundational concepts such as Markov decision processes, value functions, and Q-learning, alongside modern deep reinforcement learning approaches including deep Q-networks (DQN), policy gradients, and actor-critic algorithms. Through hands-on programming exercises using Python frameworks like OpenAI Gym and Stable Baselines, students will implement and train RL agents to solve complex control problems, game playing, and optimization tasks. By the end of the course, students will be equipped to design and deploy autonomous decision-making systems for diverse applications, from robotic control simulations to dynamic resource allocation.

Algorithms for Image and Video Generation

Algorithms for Image and Video Generation is a specialized artificial intelligence course that explores cutting-edge techniques for creating synthetic visual content using machine learning and deep learning approaches. The course covers fundamental generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), diffusion models, and autoregressive models, alongside advanced architectures for high-quality synthesis. Through hands-on implementations using frameworks like PyTorch or TensorFlow, students will build and train models for various visual tasks such as image generation, style transfer, and video prediction. By the end of the course, students will be capable of developing sophisticated visual synthesis systems and understanding the responsible application of creative AI technologies in various industries.

Practical Introduction to Visual Perception

Practical Introduction to Visual Perception is a specialized course that explores the fundamentals of human visual perception and its applications in computer vision and artificial intelligence systems. The course covers key topics including visual processing mechanisms, color perception, depth and motion perception, pattern recognition, and visual attention models. Students will master essential concepts including image formation principles, feature detection, object recognition processes, and the relationship between biological vision systems and computational models.

Deep Learning

Deep Learning is an advanced artificial intelligence course that provides students with comprehensive understanding of neural network architectures and modern deep learning techniques. The course covers fundamental concepts including multi-layer perceptrons, backpropagation, gradient descent optimization, and regularization techniques, progressing to advanced architectures such as convolutional neural networks, recurrent neural networks, transformers, and generative models. Students will master essential deep learning concepts including activation functions, loss functions, batch normalization, dropout, transfer learning, and hyperparameter tuning.

Advanced Natural Language Processing

This specialized course explores sophisticated Natural Language Processing (NLP) techniques and state-of-the-art language understanding systems beyond foundational concepts. The course covers advanced topics including in-depth transformer architectures, attention mechanisms, pre-trained language models, multilingual NLP, and specialized applications such as question answering, dialogue systems, and document understanding. Through hands-on implementations using advanced frameworks, students will develop sophisticated NLP systems for challenging real-world applications including information extraction, conversational AI, and cross-lingual text processing. By the end of the course, students will be equipped to build cutting-edge AI systems that can understand and generate human language with increasing sophistication.

Academic Training

This course is designed to equip students with the essential academic skills required for success in higher education and research-oriented environments. It emphasizes the development of critical thinking, structured academic writing, and effective communication within scholarly contexts. Students will engage with core components of academic practice, including research methodology, literature review techniques, citation standards, and ethical considerations such as academic integrity and plagiarism avoidance. The course also focuses on enhancing analytical reading and argumentation skills. Through a combination of guided instruction, practical exercises, and continuous feedback, students will learn how to construct well-reasoned academic arguments, produce high-quality written work, and deliver professional presentations.

CLCassie Lu

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