CSSE/MA 416 Deep Learning

Instructors: Boutell, Petrik & Shibberu

Course Prerequisites:
MA212, and either MA223 or MA381, and one of CHE310, CSSE220, ECE230, MA332, MA386 or (ME323 or ME327).

What is Deep Learning?

Deep Learning is the engine of today’s AI revolution. By stacking many simple computational layers, deep neural networks can sift through massive datasets and automatically uncover the high‑level patterns that humans once had to hand‑craft. This capability of Deep Learning powers self‑driving cars, medical systems that detect disease earlier than doctors, voice assistants that understand conversational speech, and real‑time language translation systems that remove language barriers.

As GPUs grow faster and data sets grow larger, the reach of Deep Learning is accelerating across mathematics, science and engineering. A key advantage that Deep Learning has over traditional machine‑learning techniques is automatic feature engineering: deep networks learn the right representation for the task at hand, enabling practitioners—regardless of domain expertise—to build state‑of‑the‑art solutions quickly.

In CSSE/MA 416 Deep Learning, you will participate in this AI wave of innovation. You will learn to train, debug, and evaluate neural networks while mastering the foundational concepts involved.

Learning Outcomes

Students will be able to:

1. Use linear and logistic regression to establish a baseline performance level for machine
   learning tasks.
2. Use transfer learning to repurpose pre-trained neural networks to new machine learning tasks.
3. Apply drop-out and early stopping regularization to prevent overfitting.
4. Determine, by hand, the number of trainable parameters in fully-connected, convolutional, and attention layers of a neural network.
5. Determine, by hand, the activation values of fully-connected, convolutional, and attention layers for given input values.
6. Apply the principle of least squares estimation to derive the optimal parameter value of a simple bias regressor.
7. Use emerging AI tools to enhance productivity.

Past Student Projects

1. Image Classification for Bird Species
2. Music Classification
3. Mars Rover Autonomous Navigation & Driving
4. Snipe Prediction
5. AI Asst Video Subtitles
6. House Prices
7. Head Orientation Image Classification
8. Chest X-ray Pneumonia Detection
9. Facial Recognition of a Group of People
10. AI + Genre Artwork Classification
11. Bird Identification
12. Flappy Bird Reinforcement Learning
13. Music Synthesis
14. Hurricane Strength
15. Human vs AI Image Classification
    Riya Bharamaraddi, Ryan Bowering, Kayla Martinez, Adithya Ramji
16. Exoplanets
    Natasa Zupanski, Zach Humes, Ash Collins, Nathen Chen
17. Judging Books by their Covers
    Swade Cirata, Nat Hurtig, Ben Joens, Andrew Kosikowski
18. Human Facial Emotion Recognition and Classification
    Angelina Amato, Bingshuo Kang, Jacob Hruska, Josh Wilmerding
19. Geoguessr Predictor
    Ethan Brown, Owen Greybill, Jared Krauss, Liam Waterbury
20. Linkedin Job Posting Predictor
    Dominic Csomos, Julian Fiorito, Kevin Lin, Luke McMahon
21. Food Processing
    Mateen Afkhami, Grant Giles, Qingyuan Jiao, Helen Wang
22. Brain Tumor Classification Using Neural Networks
    Avery Wagner, Colin Decker, Harman Singh, Noah Clippinger
23. Voice Recognition on a Real-Time Embedded System
    Bryce Bejlovec, Noah Lee, Clark Ren, Owen Sapp, Owen Trudt
24. Spotify Playlist Smoother
    Precious Saelee, Neha Vinesh, Noah Young
25. Parking Lot Car Counter
    Alejandro Marcenido, Rhian Seneviratne, Jack Aldworth, Simarjit Dhillon
26. Taylor Swift LLM
    Rebecca Breland, Spencer Chubb, Thomas Hanna, Dylan Luttrell, Ian Liu
27. Calendar Assistant
    Zachary Decker, Daniel Gaull, Garret Hart, Shaina Freeland, Alan Zhang
28. Predicting Album Genre from Cover Art
    Sal Altobellis, Sybil Chen, Dalton Julian, Braden Kattman, Braxton Lee
29. UFO Detection, Using a Deep Learning Classifier to Determine What is in the Sky, Ahmed Balubaid, Jordan Massey, Nithin Saravanapandian, Azzam Turkistani, Caleb Yankey
30. Using Deep Learning to Recognize Heavy Metal Band Names
    from Band Logos, Martino Kim, Derick Miller, Will Rasp, Arya Ziegler
31. Using Deep Learning to Predict Car Information From Car Images Project, Yutong Chen, Xianshun Jiang, Yunzhe Wei, Yujie Zhang, Travis Zheng
32. Exploring DDPG’s Capabilities Through Varying Complexity of Gym Environments, Luke Ferderer, Jayden Foshee, Sean Hyacinthe, Tristan Scheiner, Nick Von Bulow
33. Identifying Alzheimer’s and Cognitive Impairment in Brain Scans
    Abhinav Chowdavarapu, Joshua Mestemacher, Cade Parkhurst, Michael Yager
34. Author Classification using NLP,
    Jackson Hajer, Wendy Ju, Srikar Namburi, Shyam Ravishankar
35. Predicting Manga Author and Publication Year with Deep Learning,
    Heath Boyea, Stephen Payne, Caleb Schlundt, Andrea Wynn
36. Detecting Dice Roll Values From Images,
    James Kelly, Luke McNeil, Jake Milanowski, Jared Petrisko, Simon Snider
37. Music Genre Classification,
    James Brandewie, Aditya Burle, Aditya Desai, Jeremiah Wooten, Will Yelton
38. Face Mask Detection with Deep Learning,
    Doris Chen, Bowen Ding, Ainsley Liu, Cehong Wang, Yintong Zhang
39. Forecasting Stock Trends Using Financial News and Deep Learning Techniques,
    Omar Fayoumi, Shengjun Guan, Alex Ketcham, Mashengjun Li, Jiadi Wang
40. Road-Based Semantic Segmentation Experimentation pdf
    Chris Comeau, Brevin Lacy, Max Wang
41. Gesture Recognition Using Convolutional Neural Networks
    Jiafan Lin, Duncan McKee, Vanshika Reddy, Neelie Shah, Bohdan Vakhitov
42. Employing Deep Learning to Disqualify LASIK Candidates Through Corneal Topographic Data
    Alexander Bradshaw, Jessica Myers, Jacob Petrisko, William Thesken
43. Using Deep Learning to Locate and Classify White Blood Cells
    David Alba-Lopez, Zijian Huang, Jing Lin, Scott Sun, Yiyuan Wang
44. Predicting Pneumonia Based on X-Ray Images
    Landon Bundy, Carlos Feng, Arudrra Krishnan, Nigel Nie, and Jacob Woodrome
45. Detecting Malaria Using Deep Learning pdf
    Akanksha Chattopadhyay, Kathi Munoz-Hofmann, Oscar Youngquist, Yuqi Zhou
46. Emojinator Recognition and Prediction pdf
    Xiangbei Chen, Song Luo, Ming Lyu, Chen Yin
47. Denoising Music Using Encoder-Decoder Neural Networks
    Frank Hu, Gavin Karr, Matthew Lyons, Rithvik Subramanya
48. Diagnosing Pneumonia from Chest X-Rays pdf
    Valerie Liu, Cherise McMahon, Tianyi Wen, Huiruo Zou
49. Predicting Artist Influence pdf
    Adam Baker, David Saadatnezhadi, Michell Schmidt, Wesley Siebenthaler
50. Compressing Neural Networks
    Leela Pakanati, Vincent Sun, Syliva Nees, Gordon Phoon
51. Predicting a Speaker’s Accent and Origin pdf
    Mingyang Cai, Eric Liobis, Brad Pender, Garret Wight
52. Identifying the Number and Location of Blocks
    Zihan Wan, Shijun Yu
53. Used Car Value Prediction
    Yang Gao, Yifei Li, Zhuochen Liu, Yuankai Wang, Hao Yang
54. Determining Code Quality
    Maya Holeman, Christopher Keinsley, Kevin Lewis, Zachary Taylor, Yizhi Feng
55. Deep Learning to Determine Malignancy of Tumors in CT Scans
    Hussein Alawami, Jacob Beckmann, John Peterson, Johann Ryan, Tyrus Sonneborn
56. Music Genre Classification Project
    Aaron Bartee, Addison Kremer, Ishan Saraf, Tom Joyner, Kaiyu Xie
57. Predicting Flight Cancellations
    Ben Brubaker, Bob Dong, Adit Suvarna
58. Robotics: Tracking Block Operations
    Xiwen Li, Zachary Dougherty
59. Code Prediction
    Remy Bubulka, Coleman Gibson, Kieran Groble
60. Base Calling of Nanopore Sequencing Data
    Fred Zang, Olivia Zhou, Tony Grueninger
61. Generating Anime Character Pictures Using GANs
    David Lam, Zhou Zhou, Yuzong Gao
62. Lung Nodule Classification Through Convolutional Neural Networks
    Mariana Lane, Cheng Xu, Jack Richard
63. Extracting Human Voices from Noisy Environments Using Deep Learning
    Trevor Morton, Chris Sadler, Jack McClary
64. Movie Review Sentiment Analysis
    Jerry Qiu, Jarvis Zhao, Jim Yoon
65. Predict Closed Questions on Stack Overflow
    Tyler Rarick, Luke Wukusick
66. Using a Convolutional Network to Predict Future Stock Prices
    Jacob Guttman, Brandon Rudolph, Honghao Zhang
67. Recurrent Generative Adversarial Networks for Text to Image Synthesis
    Nathan Blank, Zane Geiger
68. Using Deep Reinforcement Learning to Approach Artificial General Intelligence
    Austin Derrow-Pinion, Kice Sanders, Adam Gastineau
69. TensorFlow Signal Processing
    Joel Shapiro
70. Magic the Gathering Card Image Recognition with Deep Learning
    Jeff Patterson, Dustin Lehmkuhl
71. Identifying Hand Hygiene with Deep Learning
    James Edwards
72. Deep Learning CNN for Depth Estimation
    Zachary Schafer, Patrick Sullivan
73. Unsupervised Email Classifier
    Hang Du, Runzhi Yang, Krystal Yang
74. Advanced Detection of Crop Diseases
    Matt Herboth
75. Autonomous Braking
    Ethan Petersen, Josh Laesch, Benedict Wong
76. Prediction of Vegetation Data Imagery
    Steve Trotta
77. Better Image Upscaler
    Alec Tiefenthal, Jacob Knispel, Peter Larson, Jason Lane
78. Long Short Term Memory Model Exploration
    Jared Hoffman