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University of Bristol - Computer Science Department - COMSM0045 - Applied Deep Learning
UNIT INFO
COMSM0045 - Applied Deep Learning
Unit Information
Welcome to COMSM0045. The unit introduces the students to deep architectures for learning linear and non-linear transformations of big data towards tasks such as classification and regression. The unit paves the path from understanding the fundamentals of convolutional and recurrent neural networks through to training and optimisation as well as evaluation of learnt outcomes. The unit's approach is hands-on, focusing on the 'how-to' while covering the basic theoretical foundations. For further general information, see the syllabus for the unit.
UPDATE - 22/09/2025 Welcome to the 25/26 cohort!
PLEASE NOTE: lecture content will be updated, slides below are placeholder and may change until the lecture
If you have any questions, head to the unit teams.Staff
| Michael Wray (MW) | Unit Director |
| Tilo Burghardt (TB) |
Teaching Assistants
Omar Emara (OE), Prajwal Gatti (PG), Rhodri Guerrier (RG), Sam Pollard (SP), Saptarshi Sinha (SS), Siddhant Bansal (SB), Yini Li (YL)
Unit Materials
| Wks | Monday 16:00-18:00 | Tuesday 15:00-18:00 | Labs |
| 1 |
22/09/2025 - 16:00 - Queens BLDG 1.07 Wk1 - LECTURE 1 INTRODUCTION TO THE UNIT intro slides recording BASICS OF ARTIFICIAL NEURAL NETWORKS (Queens Building 1.07, in-person) (Introduction, Neural Networks, Perceptron, Cost Functions, Gradient Descent, Delta Rule, Deep Networks) PDF Slides, Recording |
23/09/2025 - 15:00 - Queens BLDG 1.07 Wk1 - LECTURE 2 TOWARDS TRAINING DEEP FORWARD NETWORKS (MVB 1.15, in-person + lecture recap) (Network Representation, Computational Graphs, Reverse Auto-Differentiation) PDF Slides, Extra Recap Recording Lecture 2 Refresher (first part of video) |
GETTING STARTED: Register Individually on BlueCrystal4 (details see below) RECAP WORKSHEETS: -Lab0 - Python (Homework) |
| 2 |
29/09/2025 - 16:00 - Queens BLDG 1.07 Wk2 - LECTURE 3 BACKPROPAGATION ALGORITHM (Queens Building 1.07, in-person + recorded lecture) (The Backpropagation Algorithm in Full Detail, Activation Functions) PDF Slides Extra Recap Recording (second part of video) Wk2 - LECTURE 4 OPTIMISATION TECHNIQUES (Queens Building 1.07, in-person + recorded lecture) (Stochastic Gradient Descent, Nesterov Momentum, RMSProp, Newton's Method, AdaGrad, Adam, Saddle Points) PDF Slides Extra Recap Recording |
30/09/2025 - 15:00 - MVB 1.15 - PRACTICAL 1 Your first fully connected layer gradient descent stochastic gradient descent Slides Recording |
30/09/2025, (MVB 1.15) - 3hrs -BC4 Setup Lab 1 - Training your first Deep Neural Network |
| 3 |
06/10/2025 - 16:00 - Queens BLDG 1.07 Wk3 - LECTURE 5 COST FUNCTIONS, REGULARISATION AND DEPTH (Queens Building 1.07, in-person + recorded lecture) (SoftMax, Cross Entropy, L1 and L2 Regularisation, DropOut, DropConnect, Depth Considerations) PDF Slides Extra Recap Recording |
07/10/2025 - 15:00 - MVB 1.15 - PRACTICAL 2 Your first convolutional connected layer Slides Recording |
07/10/2025, (MVB 1.15) - 3hrs Lab 2 - Your First Convolutional Connected Network |
|
Wk3 - LECTURE 6 CONVOLUTIONAL NEURAL NETWORKS (Queens Building 1.07, in-person + recorded lecture) (sharing parameters, conv layers, pooling, CNN architectures) Slides Recording |
|||
| 4 |
13/10/2025 - 16:00 - Queens BLDG 1.07 Wk4 - LECTURE 7 RECURRENT and RELATIONAL NEURAL NETWORKS (Queens Building 1.07, in-person + Recorded) (RNN, encoder-decoder, Transformers) Slides Recording Pt. 1 Recording Pt. 2 |
14/10/2025 - 15:00 - MVB 1.15 -
PRACTICAL 3 Error rate monitoring (training/validation/testing) Batch-based training Learning rate Weight Freezing Batch normalisation Parameter intialisation Slides Video Recording |
14/10/2025, (MVB 1.15) - 3hrs Lab 3 - Hyperparameters |
| 5 |
20/10/2025 - 16:00 - Queens BLDG 1.07 Wk5 - LECTURE 8 GENERATIVE MODELS (Queens Building 1.07, in-person + Recorded) (Autoregressive models) Slides Recording |
21/10/2025 - 15:00 - MVB 1.15 - PRACTICAL 4 Data Augmentation Debugging strategies Dropout Slides Video Recording |
21/10/2025, (MVB 1.15) - 3hrs Lab 4 - Data Augmentation |
| 6 | READING WEEK - Mid Term for MAJOR unit students 30/10/2025 - MVB - 1.07 - 13:00-14:00 | ||
| 7 | - |
04/11/2025 - 15:00 - MVB 1.15 - PRACTICAL 5 Transformers Slides |
04/11/2025, (MVB 1.15) - 3hrs Lab 5 - Transformers |
| 8 | - | 11/11/2025 - 15:00 - MVB 1.15 - Continuation Lab |
11/11/2025, (MVB 1.15) - 3hrs |
| 9 | - | 18/11/2025, 15:00 [2 hours], (MVB 1.15) - CW Support Session |
- |
| 10 | - | 25/11/2025, 15:00 [2 hours], (MVB 1.15) - CW Support Session |
- |
| 11 | - | 02/12/2025, 15:00 [2 hours], (MVB 1.15) - CW Support Session |
- |
| 12 | - | 09/12/2025, Queens BLDG 1.07 - Exam Support Session | - |
| 13 | DECEMBER EXAMS - Final for MINOR unit students | ||
Assessment Details
- Coursework (for Major option): You are requested to re-implement work to be specified, and provide your code as well as a final report. Coursework completed in groups (up to 3)
- Mid-term test (for Major option): The theoretical components of the unit up to week 5 are examinable. No code would be written in the exam, but code can be provided to answer questions. Calculators are allowed.
- Exam (for Minor option): Both the theoretical and practical components of the unit are examinable. No code would be written in the exam, but code can be provided to answer questions. Calculators are allowed.
Assessment Details - Coursework
The coursework will be released during TB1
Assessment Details - Exam
More details regarding the exam and in-class test coming soon. You can find previous papers here, but please note that these were from when the unit only contained one 2 hour exam.Please note that you cannot take notes into the exam (it is closed book), but calculators are permitted.
Github
All technical resources will be posted on the COMSM0045 ADL Github organisation. If you find any issues, please kindly raise an issue in the respective repository.
Textbook
Recommended Reading:Simon J.D (2023). Prince. Understanding Deep Learning, MIT Press