https://joaocarlos.github.io/Hugo-academia/courses/Recent content in Courses on Dr. Jane SmithHugoenMon, 01 Jan 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/digital-design/Mon, 01 Jan 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/digital-design/Course Description This course provides a comprehensive introduction to digital logic design, covering combinational and sequential circuits, finite state machines, and FPGA implementation. Students will gain hands-on experience with hardware description languages (VHDL/Verilog) and modern design tools. Learning Objectives By the end of this course, students will be able to: Design and analyze combinational and sequential digital circuits Implement digital systems using hardware description languages Use FPGA development tools for synthesis and simulation Apply systematic design methodologies to complex digital systems Debug and verify digital circuits using industry-standard techniques Prerequisites Introduction to Programming Basic Circuit Analysis Discrete Mathematics Topics Covered Weeks 1-2 Digital Fundamentals Number systems, Boolean algebra, logic gates, and truth tables.https://joaocarlos.github.io/Hugo-academia/courses/advanced-machine-learning/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/advanced-machine-learning/Course Description This graduate-level course covers advanced topics in machine learning, including deep learning architectures, reinforcement learning, generative models, and their applications to computer vision, natural language processing, and robotics. Learning Objectives Master advanced deep learning architectures (CNNs, RNNs, Transformers) Understand and implement reinforcement learning algorithms Design and train generative models (GANs, VAEs, Diffusion) Apply ML techniques to research problems Read and critique ML research papers Prerequisites CS401: Introduction to AI Strong programming skills in Python Graduate standing or instructor approval Evaluation Component Weight Paper Reviews 20% Programming Assignments 30% Research Project 40% Presentation 10%https://joaocarlos.github.io/Hugo-academia/courses/introduction-to-artificial-intelligence/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/introduction-to-artificial-intelligence/Course Description This course provides a comprehensive introduction to artificial intelligence, covering fundamental concepts, algorithms, and applications. Students will learn about search algorithms, knowledge representation, reasoning, machine learning, and neural networks. Learning Objectives By the end of this course, students will be able to: Understand the history and foundations of AI Implement search algorithms for problem-solving Apply machine learning techniques to real-world problems Design and train neural networks Evaluate AI systems ethically and critically Prerequisites CS201: Data Structures and Algorithms MATH301: Linear Algebra Basic programming experience in Python Evaluation Component Weight Assignments (4) 30% Midterm Exam 25% Final Project 30% Class Participation 15% Schedule Week Topic Reading 1 Introduction to AI Chapter 1 2-3 Search Algorithms Chapters 3-4 4-5 Knowledge Representation Chapters 7-8 6-7 Machine Learning Basics Chapters 18-19 8 Midterm Exam - 9-10 Neural Networks Chapter 21 11-12 Deep Learning Supplementary 13-14 AI Applications & Ethics Chapter 27 15 Project Presentations - Resources Textbook: Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th Edition Software: Python 3.