605.742.81 - Deep Neural Networks

Expanded Course Description

This course is taught by Oleg Melnikov (LinkedIn) and Samuel Nathanson.

Course Structure

Course material is divided into weekly modules in Canvas. Each module runs for a period of seven (7) days and contains the following: 

1. Viewing material: instructor recorded videos and content related YouTube videos
2. Reading material: assigned chapter(s) for reading and related publication(s)
   
3. Auto-graded quizzes on textbook material and related publications. 
   
4. Team assignments and group final project.

Course Topics

We will closely follow the topics in HOML textbook (2nd edition, not newer or older!). We will rapidly advance through HOML part I to cover classical ML, build up on terminology and Kaggle/Colab/Competition skills/grading. Weekly modules (topics):

1. HOML Ch.1-2: Machine Learning Fundamentals
2. HOML 3-4: Classification and Regression
3. HOML 6-7: Decision Trees and Ensembles
4. HOML 8-9: Dimension Reduction, Unsupervised Learning
5. HOML 10: Artificial Neural Network (ANN) with Keras
6. HOML 11: Training a Deep Neural Network (DNN)
7. HOML 12: Custom Models and Training with TensorFlow (TF)
8. HOML 13: Load and Preprocess Data with TF
9. HOML 14: Deep Computer Vision (CV) and Convolutional Neural Networks (CNN)
10. HOML 15: Process Sequences Using Recurrent Neural Networks (RNN) and CNNs
11. HOML 16: NLP with RNNs and Attention
12. HOML 17: Representation Learning and Generative Adversarial Networks (GAN)
13. HOML 18: Reinforcement Learning
14. HOML 19: Train and Deploy Scale

Course Goals

To introduce the students to the variety of deep learning (DL) models, their construction, evaluation and application.
To enable students to tie Python programming code to mathematics/statistics in order to solve real world problems we face today and in the future.
To foster the development of teamwork and collaboration skills through weekly competitions, encouraging students to engage in problem-solving within a team setting, and preparing them for the collaborative nature of the modern workplace.

Course Learning Outcomes (CLOs)

• Explain applications of DNN in supervised and unsupervised settings, including DNNs’ dis/advantages.
• Evaluate different types of DNNs, including CNN, RNN, LSTM, Attention, GAN, DRL and others.
• Explain evaluation metrics of DNN in supervised and unsupervised settings, including dis/advantages of these metrics.
• Summarize the latest publications using DNN architecture.
• Distinguish dis/advantages of computational performance of DNNs on CPUs, GPUs and TPUs.
• Interpret mathematical structures and transformations that are foundational to DNN.
• Implement DNNs with Python and DNN libraries, including Keras, TensorFlow, TensorBoard, and SciKit Learn.

Textbooks

Required:

• Hands-On Machine Learning (HOML) with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition - to match the page numbers in the quizzes). Géron, A. (2019). O'Reilly Media ISBN-13: 978-1492032649
  

1. Deep learning, Ian Goodfellow, et. al. 2016. MIT Press
   
2. Pattern recognition and machine learning. Svensén, M. and Bishop, C.M., 2007.
3. Dive into deep learning. Zhang, A., Lipton, Z.C., Li, M. and Smola, A.J., 2021. arXiv:2106.11342.
4. Foundations of machine learning. Mohri, M., Rostamizadeh, A. and Talwalkar, A., 2018. MIT press.
5. An introduction to statistical learning (Vol. 112, p. 18). James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. New York: Springer.
6. The elements of statistical learning (Vol. 1, No. 10). Friedman, J., Hastie, T. and Tibshirani, R., 2001. New York: Springer series in statistics.
7. Applications of deep neural networks. Heaton, J., 2020. arXiv:2009.05673.

1. Mathematics for machine learning. Deisenroth, M.P., Faisal, A.A. and Ong, C.S., 2020. Cambridge University Press. (Source)
2. The Matrix Cookbook. Petersen, K.B. and Pedersen, M.S., 2008. Technical University of Denmark, 7(15), p.510. (Source)
3. Beginner's Guide to Python and Python Tutorial. Python.org

Other Materials & Online Resources

Students need a Google Drive account (available for free with Gmail account) with a Google Colab plugin. Colab is a Jupyter Notebook-like interface, which provides a seamless access to Python, TensorFlow, visualizations and (free) CPU/GPU/TPU hardware from Google. Colab ensures identical programming environment among all participants of the course.

Note: This is an individual (not a team) assignment. Please don't post the copyrighted material in the public space outside of our course; the content takes many hundreds of hours to create and maintain. See JHU's Code of Conduct, Honor Code, and Academic Integrity Policy. Please report suspected cheating incidents to the Teaching Team.

Required Software

We use Google Colab Notebooks for programming assignments, which is pre-configured with Python 3.x and most packages/versions already installed.

Student Coursework Requirements

Final grades will be determined by the following weighting:

1. 30%: Competitive Team Assignments
2. 30%: Auto-graded quizzes
3. 10%: Participation discussion forums, incl. publication discussions
4. 30%: Final project and presentation (e.g. Kaggle competition).
   1. All final projects are group projects. 
   2. The project is done in 2 stages - interim submission is 15% and final submission is 15% of the total grade
      

Participating teams are graded generously, receiving between 70-100% for any competition as long as they meet the benchmark and adhere to the submission guidelines, which are described in detail. It's important to note that there is no single "correct" solution in these competitions. Each week, we introduce new concepts and techniques, providing a range of approaches for you to explore. Success in these competitions is not about being the best but about engaging with the process, meeting the baseline criteria, and demonstrating effort, which can typically result in earning 80-90%.

Grades for the Kaggle competition assignments are determined as follows:

• Above-Baseline: Scores above the baseline are proportional to their rank within the 80-100% range.
• Below-Baseline: Scores below the baseline receive 70%.
• Missing Participation: Earns zero points.

The competition assignment grades are adjusted based on several factors, including:

• Team performance on the private leaderboard
• Reproducibility of the private leaderboard score
  
• Documentation and references
• Runtime speed
  
• Late penalties
  
• Notebook errors
• Use of non-student section in Colab
• Compliance with competition rules (See rules on Kaggle)

Top leaderboard scores can significantly drop if rules are not followed, while lower-performing submissions can receive higher grades if they adhere to the guidelines or excel in regards to speed, documentation, and references. Colab submissions are peer-reviewed after each competition ends.

Grading Policy

Course Evaluation

Regrading:

1. We aim to grade fairly, accurately, and timely. If you believe we made a crude grading error, make a private post in our discussion forum ASAP (within 1 week of grading) & tag a grader.
2. To discourage frivolous appeals, we reserve the right to deduct a 2-5% of the grade, if your appeal lacks a strong justification or the benefit fails to exceed 2-5%. Be sure it is worth the mutual effort.
3. If in doubt, always use the course textbook as the main source of truth. It has been well edited, provides full context, and has very few typos.

1. Rule 0: If ever in doubt, just ask!
2. Every output (plot, table, metrics, ...) must have a purpose and an interpretation.
   1. We never produce outputs for the sake of filling the void.
3. Output must be legible, readable, meaningfully organized, neat, clean, compact, precise, and concise.
4. Avoid generic terms, such as data and model. Instead, be specific and use observations, features, linear regression, logistic regression, etc.
5. No overplotting. No redundant plots. No data dumps (pages of table outputs).
6. Annotate your programming code! Help your audience to understand your abstraction.
7. All code must execute in Google Colab and Python 3.x without error/warning messages or local file dependencies.
   
8. Always start your modeling with a simple baseline model (version 0), which you can do with minimal data munging.
   1. This is your Proof of Concept and benchmark for any future improvement.
9. Start your work early! Never procrastinate. We are busy on assignment due days and may be inaccessible to answer last minute questions.
10. Clearly label problems and order them as assigned (to help us avoid missing your solutions)
11. IF late submissions are allowed, there will be 10% penalty per day. (Start early, manage your time, expect to get stuck!)
12. Quiz:
    1. Students are given 2 quiz attempts with the highest grade recorded.
       
    2. Multi-choice questions are multi-answer. There are always 4 answer choices, each can be correct or not. The number of correct answer choices can vary from 0 to 4.
    3. The student can view the question's grade and their answer selections, but not correct/incorrect while the quiz is open. After the quiz closes, a full scoring is released. Thus, the grade of <100% indicates some missed or incorrect choices.
    4. Most questions show a hint if answered with <100%. Canvas has no direct way to post solutions after the quiz closes. If marking is unclear, please make a post in Q&A Discussion (after the quiz is closed), and we'll provide more clarity in reasoning.
    5. Incorrect response lowers your score. Correct response raises your score (to avoid guessing). Lowest grade is zero. Hence, it rewards partial credit.
       1. Here are examples with 2 correct answer choices (say, A,B), assuming 2 point value of the question (i.e. 1 point for each answer choice):
          1. Ex1: Student selects A & B, all correct answer choices only. Grade=max(0,1+1)=2
          2. Ex2: Student selects 1 correct only. Grade=max(0,1+0)=1 (out of 2 points or 50%)
          3. Ex3: Student selects 1 correct and 1 incorrect. Grade=max(0,1-1)=0
          4. Ex4: Student selects 2 correct and 1 incorrect. Grade=max(0,1+1-1)=1
          5. Ex5: Student selects 1 correct and 2 incorrect. Grade=max(0,1-1-1)=0
          6. Ex6: Student selects all answer choices. Grade=max(0,1+1-1-1)=0
          7. Ex7: Student leaves all answer choices unselected. Grade=max(0,-1-1)=0
13. Grade disputes should be raised within 1 week of grade release. Please carefully monitor your grades in Canvas Gradebook. For example, Canvas may auto-apply last submission's late penalty to the highest grade for the assignment. We will not honor the correction, if it's noted outside of grade dispute interval.

Course Policies

RE: Assignments affected by illness. If you caught a flu or some other short-term sickness, we can give you an extension on quizzes and other autograded assignments (please notify the Teaching Team of your situation, the affected assignments and the proposed deadline). It's a good idea to attach whatever you have completed at the moment to have it timestamped. We cannot pause or postpone deadlines for Kaggle competitions, however. Thankfully, you can coordinate and rebalance the workload with your teammates. Remember, the team is a single entity - you win together and you lose together - as one. Just in case, please familiarize yourselves with the JHU's policy on absences due to illness.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.