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32 33<body> 34 <div class="container"> 35 36 37<section id="header-nav"> 38 <header> 39 <nav> 40 <ul> 41 42 <a href="/"> 43 <li class="btn-nav">Home</li> 44 </a> 45 <a href="/assets/pdfs/CV.pdf" target="_blank" rel="noopener noreferrer"> 46 <li class="btn-nav">CV</li> 47 </a> 48 <a href="/publications"> 49 <li class="btn-nav">Publications</li> 50 </a> 51 <li class="current">Teaching</li> 52 53 </ul> 54 </nav> 55 </header> 56</section> 57 58<div id="teaching"> 59 <section class="bg"></section> 60 <h1 class="title">Teaching</h1> 61 <br/> 62 63 64 <p class="title"> Bowdoin College </p> 65 <ul class="course-list"> 66 67 <li class="course"> (CS3485) Deep Learning for Computer Vision</li> 68 69 <div class="content"> 70 <p> <b> Course Description:</b> Computer Vision has become ubiquitous in our society, image searches to self-driving cars. On the other hand, Deep learning has shaken the world of artificial intelligence in the recent years. Most of these developments greatly advanced the performance of state-of-the-art visual recognition systems, which put Computer Vision in the epicenter of most technological progress from the past decade. In this context, this course aims at providing a consistent exploration of how deep learning started to its most recent achievements, always using Computer Vision tasks as their main application, historically or practically. During the course, we'll also understand many of the main computer vision problems and use them as cases for the introduction of various deep learning related problems. Finally, this course hopes to give students working knowledge of PyTorch, one of the main deep learning frameworks, and prepare them to future industrial and academic careers in the field.</p> 71 </div> 72 73 74 75 <span class="term"> Fall 2023 </span> 76 77 78 79 <span class="term"> Fall 2024 </span> 80 81 82 83 <span class="term"> Spring 2025 </span> 84 85 86 87 <span class="term"> Spring 2026 </span> 88 89 90 91 <span class="term"> <a href="https://jeovafarias.github.io/Bowdoin-CS3485/" class="btn btn-sm z-depth-0" role="button"> Fall 2026</a></span> 92 93 94 95 96 <li class="course"> (CS2400) Artificial Intelligence</li> 97 98 <div class="content"> 99 <p> <b> Course Description:</b> This course provides a broad introduction to foundational concepts in artificial intelligence and machine learning. We will consider the design and implementation of (apparently) intelligent computational agents inhabiting complex environments. This is a quantitative class with a focus on mathematical, logical, and algorithmic concepts, plus a significant amount of programming. Typical "AI" course topics include heuristic graph search, basic probability theory, Bayesian inference, and Markov Decision Problems. Typical "ML" course topics include fitting and overfitting models, cross-validation, classification and regression tasks, and unsupervised learning. Towards the end of the course, we will cover feed-forward neural networks, the back propagation algorithm, and as many advanced neural architectures as time allows. All along the way, we will consider how the performance measures that guide our agents might lead to unfair outcomes, plus a case study deep-dive into statistical measures that can potentially shine a light on the biases an intelligent model may have "learned".</p> 100 </div> 101 102 103 104 <span class="term"> Fall 2024 </span> 105 106 107 108 <span class="term"> Spring 2025 </span> 109 110 111
112 <span class="term"> Fall 2025 </span> 113 114 115 116 117 <li class="course"> (CS1101) Intro to Computer Science</li> 118 119 <div class="content"> 120 <p> <b> Course Description:</b> This course provides an introduction to computational thinking, programming, and the field of computer science in general. Computer science is fundamentally a study of problem solving, not simply computers (or computer programs) themselves. We consider questions such as "What defines computer science?", "How do we design an algorithm to solve a problem?", "How do we translate an algorithm into a computer program?". Over the course of the semester, students learn the fundamentals of programming using the Python programming languageLinks to an external site. and write a variety of programs during weekly homework assignments and larger projects.</p> 121 </div> 122 123 124 125 <span class="term"> Spring 2023 </span> 126 127 128 129 <span class="term"> Fall 2026 </span> 130 131 132 133 134 </ul> 135 136 <p class="title"> Haverford College </p> 137 <ul class="course-list"> 138 139 <li class="course"> (CS105) Introduction to Computer Science</li> 140 141 <div class="content"> 142 <p> <b> Course Description:</b> Introduction to computer science with an emphasis on problem-solving, programming, and algorithm design. Uses a high-level programming language for solving problems and emphasizing program design and development. Topics include basic programming techniques and approaches to algorithm design, and techniques for understanding the process of computation and the correctness of software. Python was used throughout the course.</p> 143 </div> 144 145 146 147 <span class="term"> Fall 2021 </span> 148 149 150 151 <span class="term"> Fall 2022 </span> 152 153 154 155 156 <li class="course"> (CS106) Introduction to Data Structures</li> 157 158 <div class="content"> 159 <p> <b> Course Description:</b> An introduction to the fundamental data structures of computer science -- strings, lists, stacks, queues, trees, BSTs, graphs, sets and their accompanying algorithms. Principles of algorithmic analysis and object reasoning and design are introduced using mathematical techniques for the notions of both complexity and correctness. More practical issues, such as memory management and hashing, are also covered. The programming language used to illustrate and implement these concepts will be able to support functional, imperative and object-oriented approaches. For these years, we used Java.</p> 160 </div> 161 162 163 164 <span class="term"> Spring 2022 </span> 165 166 167 168 <span class="term"> Spring 2023 </span> 169 170 171 172 173 <li class="course"> (CS260) Foundations of Data Science <i> (Co-instructor)</i></li> 174 175 <div class="content"> 176 <p> <b> Course Description:</b> This course introduces core principles of learning from data. More and more decisions are being made by algorithms that operate on large datasets, and this course will give students the tools to understand and contribute to this process. Throughout we emphasize the ethical use of data and analyze case studies of how data science has intersected with society. This course has a significant theory component, covering introductory linear algebra, probability, statistics, modeling, information theory, and optimization. However, we implement these ideas (in Python) and apply them to concrete datasets from a variety of fields (including images, video, text, DNA, music, art, etc). Course designed by <a href="https://smathieson.sites.haverford.edu/" class="btn btn-sm z-depth-0" role="button">Professor Sara Mathieson.</a></p> 177 </div> 178 179 180
181 <span class="term"> Fall 2021 </span> 182 183 184 185 186 <li class="course"> (CS360) Machine Learning <i> (Co-instructor)</i></li> 187 188 <div class="content"> 189 <p> <b> Course Description:</b> To explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as TensorFlow and Keras. By the end of this course, students should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data. Course designed by <a href="https://agrissom.sites.haverford.edu/" class="btn btn-sm z-depth-0" role="button">Professor Alvin Grissom II.</a></p> 190 </div> 191 192 193 194 <span class="term"> Spring 2022 </span> 195 196 197 198 199 <li class="course"> (CS396) Deep Learning for Computer Vision</li> 200 201 <div class="content"> 202 <p> <b> Course Description:</b> Computer Vision has become ubiquitous in our society, image searches to self-driving cars. On the other hand, Deep learning has shaken the world of artificial intelligence in the recent years. Most of these developments greatly advanced the performance of state-of-the-art visual recognition systems, which put Computer Vision in the epicenter of most technological progress from the past decade. In this context, this course aims at providing a consistent exploration of how deep learning started to its most recent achievements, always using Computer Vision tasks as their main application, historically or practically. During the course, we'll also understand many of the main computer vision problems and use them as cases for the introduction of various deep learning related problems. Finally, this course hopes to give students working knowledge of PyTorch, one of the main deep learning frameworks, and prepare them to future industrial and academic careers in the field.</p> 203 </div> 204 205 206 207 <span class="term"> Fall 2022 </span> 208 209 210 211 212 </ul> 213 214 <p class="title"> Brown University </p> 215 <ul class="course-list"> 216 217 <li class="course"> Artificial Intelligence and Deep Learning</li> 218 219 <div class="content"> 220 <p> <b> Course Description:</b> A summer course designed to introduce sudents to the concepts of supervised learning, optimization and neural networks. The students were not expected to have any prior contact with programming before this class. At the end of it, they would be able to run simple neural network based classification algorithms using Keras.</p> 221 </div> 222 223 224 225 <span class="term"> Summer 2019 </span> 226 227 228 229 230 </ul> 231 232
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