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 - 24/06/2025 Currently Under Construction for 25/26! 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.Michael Wray (MW) | Unit Director |
Tilo Burghardt (TB) |
Omar Emara (OE), Prajwal Gatti (PG), Rhodri Guerrier (RG), Sam Pollard (SP), Saptarshi Sinha (SS), Siddhant Bansal (SB), Yini Li (YL)
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 PDF 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 |
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 |
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 |
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 |
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 |
21/10/2025 - 15:00 - MVB 1.15 - PRACTICAL 4 Data Augmentation Debugging strategies Dropout Slides |
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 |
The coursework will be released during TB1
Please note that you cannot take notes into the exam (it is closed book), but calculators are permitted.
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.
Recommended Reading:Simon J.D (2023). Prince. Understanding Deep Learning, MIT Press