Main Second Level Navigation
- Research stream programs: prospective students
-
Research stream programs: current students
- Course requirements and performance expectations
- Thesis Advisory Committee
-
Graduate course list
- LMP1001/1002/1003: Graduate Seminars in Laboratory Medicine and Pathobiology
- LMP1005H: Fundamentals of Research Practice
- LMP1100H: Cellular imaging in pathobiology
- LMP1101H: Basic concepts in inflammatory/autoimmune arthritis
- LMP1102H: Clinical concepts in inflammatory/autoimmune arthritis
- LMP1103H: Tissue injury, repair and regeneration
- LMP1105: Current understanding of Atherosclerosis
- LMP1106H: Molecular Biology Techniques
- LMP1107H: Bioinformatics in LMP
- LMP1108H: Genome analysis in medicine
- LMP1110H: Neural Stem Cells - brain development and maintenance
- LMP1111: Introduction to R and the Analysis of Single Cell Data
- LMP1200H: Neoplasia
- LMP1203H: Basic principles of analytical clinical biochemistry
- LMP1206H: Next generation genomics in clinical medicine
- LMP1207H: Mass spectrometry, proteomics and their clinical applications
- LMP1208H: Molecular clinical microbiology and infectious diseases
- LMP1210H - Basic Principles of Machine Learning in Biomedical Research
- LMP1211H: Foundations in Musculoskeletal Science
- LMP2004H: Introduction to Biostatistics
- Fees, stipends, awards & grants
- Program transfers
- LMP Workshop Program
- Time off, leave and withdrawals
- Academic appeals
- Program completion for MSc and PhD
- Graduate forms
- Student services and support
- Communicate your research: the 3MT in LMP
- Mentoring & professional development for graduate students
- Master of Health Science (MHSc) in Laboratory Medicine
- Master of Science in Applied Computing (MScAC) Artificial Intelligence in Healthcare
- Collaborative Specialization in Musculoskeletal Sciences (CSMS)
- Master of Health Science (MHSc) in Translational Research
- Student Union: CLAMPS
Breadcrumbs
- Home
- Graduate
- Research stream programs: current students
- Graduate course list
- LMP1210H - Basic Principles of Machine Learning in Biomedical Research
LMP1210H: Basic Principles of Machine Learning in Biomedical Research
Who can attend
You must be registered in a graduate program to attend this course.
This course is open to all graduate students at the University of Toronto, provided you have pre-approval from your department and the course coordinators.
Course description
This course is intended for graduate students in Health Sciences to learn the basic principles of machine learning in biomedical research and to build and strengthen their computational skills of medical research. The goal is to establish an essential foundation for graduate students to take the first steps in computational research in medicine.
The course aims to equip you with the fundamental knowledge of machine learning (ML). During the course, you will acquire basic computational skills and hands-on experience to deploy ML algorithms using python. You will learn the current practices and applications of ML in medicine, and understand what ML can and cannot do for medicine.
- Introduction to basic principles and current practices of machine learning in biomedical research.
- Focus on the fundamental ML algorithms with applications in biomedical data
- The application of unsupervised learning in genomic data
- The application of supervised learning for medical images.
Course coordinators
lmp.grad@utoronto.ca for administrative queries.
Timings and location
Thursdays, 10:30 am - 12:30 pm
Location: BA1210 (Bahen Centre Information Tech)
Evaluation methods
- Three assignments (45%)
- Term project on machine learning algorithms in medicine (40%)
- In-class participation (15%)
Schedule
Date |
Topic |
---|---|
January 11, 2024 |
Intro to ML in medicine, nearest neighbor classifier |
January 18, 2024 |
Linear methods for regression and classification; tree-based classifier Math diagnostic due |
January 25, 2024 |
Introduction to Python; basic linear algebra; evaluation methods |
February 1, 2024 |
ENSEMBLE-based methods; neural networks 1st assignment due |
February 8, 2024 |
Supervised learning; Python tutorial for supervised learning practice |
February 15, 2024 |
Unsupervised learning for clustering: K-means, Gaussian mixture models 2nd assignment due |
February 22, 2024 |
Reading week (no class) |
February 29, 2024 |
Unsupervised learning for clustering: auto-encoder, graph-based methods; Python tutorial for unsupervised learning practice |
March 7, 2024 |
Guest Lecturer: TBD 3rd assignment due |
March 14, 2024 |
Guest Lecturer: TBD |
March 21, 2024 |
Advanced deep learning methods for medical image analysis |
March 28, 2024 |
Term project in-class presentation |
April 4, 2024 |
Term project in-class presentation |