Green Biotec UG Bremen Germany
Deep Learning in the Life Sciences
Deep Learning in the Life Sciences |
|
|---|---|
| Course Description | This course introduces foundations and state-of-the-art machine learning challenges in genomics and the life sciences. We introduce deep learning approaches to key problems, comparing and contrasting their power and limitations. We seek to enable students to evaluate various solutions to crucial problems we face in this rapidly developing field and to execute new enabling solutions that can have a large impact. |
| Recommended Books | 1. Dive Into Deep Learning, A. Zhang, Z.C. Lipton, M. Li, A. Smola. 2. Deep Learning by Goodfellow, Bengio, and Courville |
| Course Learning Outcomes | After completing this course, a student will be able to: 1. Be knowledgeable about deep learning methods in life sciences, especially in tasks like sequence and structure analysis and evolution, biological networks 2. Understand the key algorithms for the main tasks 3. Implement and apply the techniques to real-world datasets |
| Assessment System | Quizzes: 10-15% Assignments: 5-10% Midterms: 30-40% ESE: 40-50% |
| Lecture Plan | ||
|---|---|---|
| S.No. | Description | Quizzes/Assignment |
| 1 | Overview of the Course/ ML Review | |
| 2 | Neural Networks (CNN (review) | Quiz 1 |
| 3 | Neural Networks (RNN & GNN) | |
| 4 | Neural Networks (Deconvolutional Networks) | |
| 5 | Interpretability, Dimensionality Reduction | |
| 6 | Relevance Propagation, Convolution Arithmetic | |
| 7 | Maximum entropy methods | |
| 8 | Discriminative Localisation | Quiz 2 |
| 9 | Interpreting ML Models: visualise Filters, Measure Gradients, Perturb inputs | |
| 10 | Tensor Flow Introduction | |
| 11 | Deep Learning Problems and compute solutions; Genomic regulatory codes | |
| 12 | Deep Learning Problems and compute solutions; Gene regulation – Single Cell RNA-seq | |
| 13 | Deep Learning Problems and compute solutions; Genetic Variations & Diseases | Quiz 3 |
| 14 | Graphs & Proteins • Protein structure prediction • Functional classifications | |
| 15 | Biomedical imaging • Video Processing, structure determination | |
