FAQ
What will be the format of the final examination?
- When: April 10, 2025, 7:00 PM - 10:00 PM, CRX C407.
- Format: To evaluate retention, this examination will be conducted as a closed-book test.
- Scope: All the lectures and assignments.
- Content: The final evaluation is designed to assess your comprehension and retention of the material presented during the whole semester. Given that there were three lectures focused on essential cell biology, you should expect questions pertaining to these topics. The questions will be directly derived from the lecture content, ensuring that answers can be found within the lecture notes.
- Question Types: The examination will predominantly consist of multiple-choice questions, supplemented by a few short-answer questions.
- Number of Questions: As this is a 3 hours examination, you should anticipate approximately 50 to 75 questions, equating to roughly 3 or 4 questions per lecture.
- In class: Please arrive on time so that we can start as early as possible.
- Student ID Card: Please bring your student ID card.
What will be the format of the midterm examination?
- When: February 25, 2025. Refer to the course Schedule page.
- Format: To evaluate retention, this examination will be conducted as a closed-book test.
- Scope: Lectures 1 to 12.
- Content: The midterm evaluation is designed to assess your comprehension and retention of the material presented during the first half of the course lectures. Given that there were three lectures focused on essential cell biology, you should expect questions pertaining to these topics. The questions will be directly derived from the lecture content, ensuring that answers can be found within the lecture notes.
- Question Types: The examination will predominantly consist of multiple-choice questions, supplemented by a few short-answer questions.
- Number of Questions: As this is an in-class assessment, you should anticipate approximately 25 questions, equating to roughly 2 to 3 questions per lecture.
- In class: You will take the quiz in class on a paper questionnaire. Please arrive on time so that we can start as early as possible (the total time available depends on your arrival time). We must collect all copies 10 minutes before the end of the class to allow the next class to begin on time.
- Student ID Card: Please bring your student ID card.
How to Effectively Prepare for Quizzes and Examinations?
- Thoroughly Review Lecture Materials:
- Review both the presentations and their accompanying Jupyter Notebooks, as they contain complementary information.
- Master Key Concepts:
- Carefully study the lecture notes to gain a comprehensive understanding of essential concepts. These include the definitions and principles of essential cell biology and machine learning, training linear models such as logistic regression, and techniques for model fitting and evaluation. Delve into cross-validation methods, and hyperparameter tuning.
- Summarize and Synthesize Information:
- Create your own summaries or concept maps for each lecture to reinforce comprehension and retention.
- Engage Deeply with Code:
- Analyze Code Examples: Examine all code snippets from lectures line by line to understand their contributions to overall functionality.
- Experiment with Code: Modify parameters and functions in the code to observe the effects of these changes, enhancing your understanding.
- Reimplement Algorithms: Try implementing key algorithms from scratch without consulting your notes.
- Develop Original Questions:
- Formulate your own questions based on lecture content and exchange them with peers for additional practice.
- Interpret Graphs and Visuals:
- Analyze graphs presented in lectures, such as those depicting algorithm performance.
- Apply and Analyze Concepts:
- Apply learned algorithms to novel scenarios.
- Analyze algorithm performance, considering factors like time complexity and optimality, to understand why certain algorithms excel in specific contexts.
- Explore Hyperparameter Tuning:
- Understand techniques like grid search and cross-validation by applying them to new datasets.
- Interpret cross-validation results, focusing on metrics such as mean accuracy and standard deviation.
- Engage in Collaborative Learning:
- Form study groups to discuss complex concepts, collaboratively solve problems, and teach topics to one another, which aids in solidifying your understanding.
- Practice with Real-world Datasets:
- Utilize datasets from OpenML, such as ‘diabetes’, to practice tasks like data splitting, model training, and evaluation.
- Experiment with different data preprocessing steps to observe their impact on model performance.
- Prepare for Code Analysis Questions:
- Anticipate questions that require predicting code outputs or identifying errors.
- Manage Your Study Time:
- Create a structured study schedule that allocates more time to challenging topics.
- Seek Clarifications:
- Proactively ask questions or seek clarification on any uncertainties well before the exam.
- Utilize office hours or discussion forums for additional support.
- Maintain a Balanced Lifestyle:
- Ensure adequate rest, nutrition, and exercise to keep your mind sharp.
- Utilize Stress Management Resources:
Is it permissible to enroll in both CSI 5180 W00 and CSI 5180 X00 concurrently and receive academic credit for each?
Certainly, students can enroll in both CSI 5180 W00 and CSI 5180 X00 concurrently and receive credit for each. CSI 5180 is categorized as a “topics course,” a flexible course code that allows faculty to introduce new subjects without the lengthy process of developing a formal course, which requires Senate approval and can take up to 18 months. This approach enables instructors to gauge student interest and demand for a subject over a period of 2 to 3 years. Importantly, the sections W00 and X00 are distinct from one another, permitting students to earn credits for both.
What level of biological knowledge is required for this course?
Understanding biology is crucial since bioinformatics aims to address real-world problems. To ensure everyone is on the same page, we will dedicate at least two lectures to cover essential concepts of molecular biology of the cell. Moreover, we will continuously revisit these concepts throughout the course as new problems are introduced. At a minimum, you should have a keen interest in learning more about biology.
Is previous experience in bioinformatics necessary?
No prior experience in bioinformatics is required. I have been teaching a course titled Algorithms in Bioinformatics (CSI 5126) for several years, which focuses on the data structures and algorithms fundamental to bioinformatics applications. However, in this course, we will pivot towards using machine learning approaches rather than traditional algorithmic methods, so no background in bioinformatics is needed.
What foundational knowledge is expected for this course?
To make this course comprehensive and self-sufficient, I do not assume any prior knowledge of machine learning. Nonetheless, a basic grasp of probability and statistics, along with calculus and linear algebra, is essential. You are also expected to be proficient in programming with a high-level language, particularly Python.