Course website for the Spring 2022 edition of Zoo4926 (section 4G55) / Zoo6927 (section 5F55), AI in Biology. Covering applications of AI in Biology.
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Course Description

Image of futuristic AI and Biological specimens

Artificial Intelligence (AI) as a field of research has existed since at least the 1950s. After initial enthusiasm, the gains of early years slowed and AI entered what has been referred to as an AI winter. Modern computing hardware, rapid growth in data collection and availability, and advances in algorithms have renewed interest in AI and revolutionized the field. AI is rapidly becoming ubiquitous in daily life and in diverse academic fields. This course will examine the applications of AI with particular focus on applications in biology. We will address the topics of what AI is, how intelligent computers really are and may become, where limitations still exist, and how AI technologies can be used to advance biological research.

The course will attempt to provide sufficient background and foundations so that students understand AI algorithms at a conceptual level, but will not focus on the mathematical details. This is not a computer science or mathematics course.

Classes will have some lecture, though most classes will consist of live coding demos and hands-on exercises.


Matt Gitzendanner
Office: Dickinson Hall, stop at front desk and they will call me

About: Dr Gitzendanner’s background is in plant evolutionary genetics and genomics where he uses genetic tools to study the conservation, evolution, and diversity of plants. The field is generally computationally intensive, and Matt has worked for 10 years training users how to use HiPerGator and other high-performance computing systems to do the amazing research that is done across the University of Florida campus.

The initial version of this course, taught in Spring 2021, was co-developed with Brian Stucky.

In addition to this course, Matt teaches

Matt enjoys spending time outdoors, hiking, backpacking and kayaking.


Computer programming

The course assumes a basic understanding of computer programming in general, and Python in particular.

If you have not taken a programming course or are new to Python, there are several LinkedIn Learning courses that will give you sufficient background to be ready for this course (these are free for UF Students):


You should have a general understanding of probability and statistics at the level of a first applied statistics course.

Knowledge of basic calculus and, to a lesser extent, linear algebra, can be helpful. We won’t focus on the math, but having a conceptual understanding of derivatives, function optimization, and matrix math will be useful.

If you are unsure, contact the instructor.

Meeting Times

  • Mon, Wed, Fri from 1:55pm - 2:45pm in Bartram 211

  • I understand that some students will need to miss classes sometimes. That is fine and I will do my best to help you catch up, but regular attendance is the best way to learn.

  • While the University has not made any statements about COVID protocols for Spring 2022, I expect that some students will want to participate remotely. At this time, I plan to use Zoom for hybrid presentation of the course and will record all sessions. The links are in Canvas.

Help Session Times

Help Session icon
I am happy to meet in-person or via Zoom. The Zoom link will be in Canvas
  • Tuesdays from 2:30 pm to 3:30 pm
  • Wednesdays from 11:00am to noon
  • Email Matt to setup a different time

Course Textbooks

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While we will not use any one text for the course, we will use sections of these books and other free resources. All will be free online resources.

Course Calendar

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For readings, there may be links to pages with my notes and additional explanations on the content from the texts.

Week Date Reading/Assignment Topic
1 Wed, Jan 5 Not required reading, but a fun intro to AI: People’s Guide to AI by Mimi Ọnụọha and Mother Cyborg (Diana Nucera) Course introduction (slides)
1 Fri, Jan 7 Take the HiPerGator Account training Brief intro and history of AI slides
* Origins of AI as an academic discipline.
* A repeating pattern: major hype and enthusiasm followed by an AI “winter”.
* Where are we now?
* Constant need to question!
2 Mon, Jan 10 PDHS Image Ch 1 of PDSH: IPython: Beyond Normal Python Introduction to Jupyter and the notebook Introduction to Jupyter
2 Wed, Jan 12   Introduction to Python
2 Fri, Jan 14 Problem set 1 available, due Friday, January 28 Introduction to Python
3 Mon, Jan 17   MLK: No Class
3 Wed, Jan 19   Git and Github
Finishing Introduction to Python
3 Fri, Jan 21 PDHS Image Ch 2 of PDSH: Introduction to NumPy Introduction to NumPy
4 Mon, Jan 24 PDHS Image Ch 3 of PDSH: Data Manipulation with Pandas
Problem Set 2 available, due Friday, February 4
Introduction to Pandas
Data Visualization in Pandas
4 Wed, Jan 26   Writing readable code, numpy array arithmetic, data visualization in Python
4 Fri, Jan 28 PDHS Image Section 5.01 of PDSH: What is Machine Learning?
Problem Set 1 due
What is Machine Learning slides
Introducing Scikit-Learn
5 Mon, Jan 31 PDHS Image Section 5.02 of PDSH: Introducing Scikit-Learn Introducing Scikit-Learn
5 Wed, Feb 2 PDSH Image Section 5.03 of PDSH: Hyperparameters and Model Validation Bias/variance tradeoff, model validation, cross-validation, and hyperparameters
See also slides
5 Fri, Feb 4 Problem Set 2 due Bias/variance tradeoff, model validation, cross-validation, and hyperparameters
See also slides
6 Mon, Feb 7 Skim PDSH Image Section 5.04 of PDSH: Feature Engineering Bias/variance tradeoff, model validation, cross-validation, and hyperparameters
See also slides
6 Wed, Feb 9 PDHS Image Section 5.06 of PDSH: In Depth: Linear Regression
Problem set 3 available, due Wednesday, February 16
Linear Regression–Lasso and Ridge Regression
6 Fri, Feb 11   Hands-on data analysis, problem set help
7 Mon, Feb 14   Classification, logistic regression
7 Wed, Feb 16 PDHS Image Section 5.07 of PDSH: In-Depth: Support Vector Machines)
Problem Set 3 due
Support vector machines
7 Fri, Feb 18   Hands-on SVMs
Work through either:
- Tutorial: image classification with scikit-learn
Remote Sensed Hyperspectral Image Classification With The Extended Morphological Profiles and Support Vector Machines
8 Mon, Feb 21 PDHS Image Section 5.08 of PDSH: Decision Trees and Random Forests)
Problem set 4 available, due Friday, February 28
Decision Trees and Random Forests
8 Wed, Feb 23   Random Forests and Ensemble Methods
8 Fri, Feb 25   Intro to Artificial Neural Networks: Lecture 05 and Notebook
9 Mon, Feb 28 Problem Set 4 due Multi-Layer Neural Networks
9 Wed, Mar 2   Convolutional Neural Networks and Lect_06
9 Fri, Mar 4   Convolutional Neural Networks
10 Mon, Mar 7   No Class: Spring Break
10 Wed, Mar 9   No Class: Spring Break
10 Fri, Mar 11   No Class: Spring Break
11 Mon, Mar 14 Problem set 5 available, due Monday, March 21 Transfer Learning
11 Wed, Mar 16   Time Series Analysis with RNNs
11 Fri, Mar 18   Time Series Analysis with RNNs
12 Mon, Mar 21   Time Series Analysis with RNNs: work through this tutorial (data and notebook located at blue_zoo4926/share/SoyBean_TS/ or work on assignment 5.
12 Wed, Mar 23   Natural Language Processing
12 Fri, Mar 25 Problem Set 5 due Natural Language Processing
13 Mon, Mar 28   Mamba and Custom Kernels
Vision Transformers intro and option 1 Vision Transformers on Casava diseases
13 Wed, Mar 30   Review of past couple weeks
13 Fri, Apr 1   No Class today work on project ideas
14 Mon, Apr 4 Problem set 6 available, due Friday, April 11 AlphaFold background
14 Wed, Apr 6   AlphaFold hands on
14 Fri, Apr 8   Topic TBD
15 Mon, Apr 11 Problem Set 6 due Project time
15 Wed, Apr 13   Project time
15 Fri, Apr 15 Project Due Project time
16 Mon, Apr 18   Project Presentations
16 Wed, Apr 20   Project Presentations

Software and Hardware

Participants will need a computer with internet connection, webcam and microphone for all classes.

Several free/open source software packages will be used throughout the course, and students will be required to install some of these. Students will use a (free) Research Computing account to access HiPerGator for coursework. Students will be required to apply for a (free) account for coursework.

If you have technical difficulties with Canvas, please contact the UF Helpdesk at:

  • (352) 392-HELP (4357)
  • Walk-in: HUB 132

Any requests for make-ups due to technical issues should be accompanied by the ticket number received from the Help Desk when the problem was reported to them. The ticket number will document the time and date of the problem. Please e-mail the instructor within 24 hours of the technical difficulty if you wish to request a make-up.

All faculty, staff and student of the University are required and expected to obey the laws and legal agreements governing software use. Failure to do so can lead to monetary damages and/or criminal penalties for the individual violator. Because such violations are also against University policies and rules, disciplinary action will be taken as appropriate.


Assignment Values

See also the List of Graded Work page.

Item Undergraduate Points Graduate Points
Problem Sets 5 @ 20 points each: 100 points (77%) 6 @ 30 points each: 180 points (75%)
Class Project 20 points (15%) 40 points (17%)
Project presentation 10 points (8%) 20 points (8%)

Undergraduates will have 5 problem sets worth 20 points each and slightly less weighting on the project. Graduates will have one extra question for each problem set (making each worth 30 points), one extra problem set and slightly higher weighting on the project.

Grading in this class is consistent with UF policies available at:

Should a student wish to dispute any grade received in this class (other than simple addition errors), the dispute must be in writing (via email) and be submitted to the instructor within a week of receiving the grade.

The dispute should clearly set out the grade that the student believes the assignment should have received as well as why they believe that they should have received such a grade.

Some assignments may be resubmitted for revision. I may suggest a resubmission, or a student may ask for the opportunity to resubmit. My goal in allowing resubmission is to give students a chance to learn the material. As meeting to discuss the material is generally expected prior to resubmission.
A A- B+ B B- C+ C C- D+ D D- F
Note: A grade of C- is not a qualifying grade for major, minor, Gen Ed, or College Basic distribution credit. For further information on UF's Grading Policy, see:

Learning Outcomes

  1. Students will be able to summarize major events in the history of AI from the 1950s to present.
  2. Students will be comfortable launching Jupyter Notebooks on HiPerGator, requesting appropriate resources for the task.
  3. Students will be able to analyze and visualize complex tabular data with NumPy, Pandas, and matplotlib
  4. Students will be able to calculate linear regression using machine learning approaches using Scikit-learn
  5. Students will be able to explain the bias/variance tradeoff
  6. Students will be able to assess ML/AI models, conduct cross-validation and tune hyperparameters
  7. Students will be able to use support vector machines, decision trees, random forests and ensemble methods to analyze data
  8. Students will be able to code a simple single-neuron perceptron from scratch
  9. Students will code multi-layer neural networks
  10. Students will conduct computer vision experiments using convolutional neural networks
  11. Students will explore times series data with recurrent neural networks
  12. Students will understand the need for and utility of transfer learning
  13. Students will gain a basic understanding of natural language processing, including tokenization, word embeddings, and the rise of transformer architectures.
  14. Students will apply transformers to computer vision tasks
  15. Students will explore protein folding prediction

Course Policies

Class Attendance and Makeup Policy

Requirements for class attendance and makeup assignments, and other work in this course are consistent with university policies that can be found in the online catalog at:

In general, I do not take attendance. You are all adults and I assume you are taking the class the learn. The best way to learn is to regularly attend class. I are sure students will miss class for various reasons. I am happy to help you catch up. If you regularly miss class and fall behind, I may ask that you hold questions on content you have missed until after class, or ask that you coordinate a time to go over the content. I will make every effort to record and post all classes to help those that miss classes.

Assignment Policy

Assignment dates will be announced at least one week in advance and students will have at least three days to complete the assignment. Each assignment will clearly state if it is an individual or group assignment. Individual assignments must be the student’s own work, completed without the assistance of others.

All assignments are “open book, open internet”, you may use whatever resources you desire to complete the assignment. Though only assignments specifically noted as group assignments should be worked on with other people.

Makeup and Late policy

Please notify the instructor of circumstances that lead to late work or missed classes. I will generally work with you and accept late work. All assignments are designed for both your own learning and my assessment of your efforts. Much of the course builds on previous sections and falling behind on assignments will make it harder to keep up. If you need help, please ask! My goal is for all students to learn the material and I understand that some students will need more help than others. The grade is based on the end product, not the amount of time and help needed to get there.

Students Requiring Accommodations

Students with disabilities requesting accommodations should first register with the Disability Resource Center (352-392-8565, by providing appropriate documentation. Once registered, students will receive an accommodation letter which must be presented to the instructor when requesting accommodation. Students with disabilities should follow this procedure as early as possible in the semester.

Course Evaluation

Students are expected to provide professional and respectful feedback on the quality of instruction in this course by completing course evaluations online via GatorEvals. Guidance on how to give feedback in a professional and respectful manner is available at Students will be notified when the evaluation period opens, and can complete evaluations through the email they receive from GatorEvals, in their Canvas course menu under GatorEvals, or via Summaries of course evaluation results are available to students at

Class Demeanor and Netiquette

Students are expected to be in class on time and behave in a manner that is respectful to the instructors and to fellow students.

Opinions held by other students should be respected in discussion, and conversations that do not contribute to the discussion should be held at minimum, if at all.

Students should be working on course content during class.

Discussion Boards

The GitHub discussion boards can be used to ask for and provide help by all. Students should be supportive and considerate of others at all times. Rude or inappropriate comments will be removed and the poster will be warned.

University Honesty Policy

UF students are bound by The Honor Pledge which states:

We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honor and integrity by abiding by the Honor Code. On all work submitted for credit by students at the University of Florida, the following pledge is either required or implied: “On my honor, I have neither given nor received unauthorized aid in doing this assignment.”

The Honor Code specifies a number of behaviors that are in violation of this code and the possible sanctions. Furthermore, you are obligated to report any condition that facilitates academic misconduct to appropriate personnel. If you have any questions or concerns, please consult with the instructor or TAs in this class

Resources are available on-campus for students having personal problems or lacking clear career and academic goals. The resources include:
  • UF Counseling & Wellness Center, 3190 Radio Rd, 392-1575, psychological and psychiatric services.
    • Provides counseling and support as well as crisis and wellness services including a variety of workshops throughout the semester (e.g., Yappy Hour, Relaxation and Resilience).
    • Many students experience test anxiety and other stress related problems. “A Self Help Guide for Students” is available through the Counseling Center (301 Peabody Hall, 392-1575) and at their web site:
    • U Matter, We Care: If you or a friend is in distress, please contact or 352 392-1575 so that a team member can reach out to the student.
  • Career Connections Center, Reitz Union, 392-1601,, connects job seekers with employers and offers guidance to enrich your collegiate experience and prepare you for life after graduation.
  • University Police Department: 392-1111 or 9-1-1 for emergencies.
  • Sexual Assault Recovery Services (SARS): Student Health Care Center, 392-1161.
  • Student Health Care Center: Call 352-392-1161 for 24/7 information to help you find the care you need, or visit
  • Food insecurity: The Pantry is a resource on the University of Florida campus committed to supporting students, staff, and faculty who are experiencing food insecurity. These individuals do not have reliable access to nutritious foods for themselves and their families. If you, or anyone you know, is experiencing food insecurity, the Pantry is a resource to visit. We offer non-perishable food, toiletries and fresh produce grown at the Field and Fork Gardens during certain times of the year. There is no proof of need required in order to shop at the Pantry; you must only bring in your valid UFID card. At the Pantry, we know that a good meal makes for a good student, and we work to support all Gators who are experiencing food insecurity. Field & Fork Food Pantry.

Inclusive Learning Environment

This course embraces the University of Florida’s Non-Discrimination Policy, which reads:

The University shall actively promote equal opportunity policies and practices conforming to laws against discrimination. The University is committed to nondiscrimination with respect to race, creed, color, religion, age, disability, sex, sexual orientation, gender identity and expression, marital status, national origin, political opinions or affiliations, genetic information and veteran status as protected under the Vietnam Era Veterans’ Readjustment Assistance Act.

If you have questions or concerns about your rights and responsibilities for inclusive learning environment, please see the instructor or refer to the Office of Multicultural & Diversity Affairs website:


There are federal laws protecting your privacy with regards to grades earned in courses and on individual assignments. For more information, please see:

Statement Regarding Course Recording

Our class sessions may be audio visually recorded for students in the class to refer back to and for use of enrolled students who are unable to attend live. Students who participate with their camera engaged or utilize a profile image are agreeing to have their video or image recorded. If you are unwilling to consent to have your profile or video image recorded, keep your camera off and do not use a profile image. Likewise, students who un-mute during class and participate verbally are agreeing to have their voices recorded.  If you are unwilling to consent to have your voice recorded during class, you will need to keep your mute button activated. As in all courses, unauthorized recording and unauthorized sharing of recorded materials is prohibited.

Dean of Students Office

Dean of Students Office (352-392-1261) provides a variety of services to students and families, including Field and Fork (UF’s food pantry) and New Student and Family programs</p>

Disability Resource Center

  • Disability Resource Center ( | 352-392-8565) helps to provide an accessible learning environment for all by providing support services and facilitating accommodations, which may vary from course to course. Once registered with DRC, students will receive an accommodation letter that must be presented to the instructor when requesting accommodations. Students should follow this procedure as early as possible in the semester.

Multicultural and Diversity Affairs

Multicultural and Diversity Affairs (352-294-7850) celebrates and empowers diverse communities and advocates for an inclusive campus.

Office of Student Veteran Services

Office of Student Veteran Services (352-294-2948 | assists student military veterans with access to benefits.


ONE.UF is the home of all the student self-service applications, including access to:

Official Sources of Rules and Regulations

The official source of rules and regulations for UF students is the Undergraduate Catalog and Graduate Catalog. Quick links to other information have also been provided below.

Procedure for Conflict Resolution

Any classroom issues, disagreements or grade disputes should be discussed first between the instructor and the student. If the problem cannot be resolved, please contact the (Under)Graduate Coordinator or the Department Chair. Be prepared to provide documentation of the problem, as well as all graded materials for the semester. Issues that cannot be resolved departmentally will be referred to the University Ombuds Office (; 392-1308) or the Dean of Students Office (; 392-1261). For further information refer to (for residential classes) or (for online classes).