Master Award in
Deep Learning
Master Award could transfer 20 credits and 50% tuition fees to Master’s programs of UKeU and/or Partner University.
Master Award in Deep Learning
The aim of this award is provides a deep understanding of deep learning concepts and methodologies, covering applications in computer vision, NLP, and graph-based tasks. Learners will evaluate techniques, understand their limitations, and apply them to real-world problems, gaining the skills for deep learning research and practice.
Could transfer 20 credits and 50% tuition fee to the Master of Artificial Intelligence of UKeU.
Learning Outcomes:
1. Understand the underlying theoretical concepts of modern deep learning methods.
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1.1 Describe the fundamental principles of deep learning.
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1.2 Explain the differences between supervised and unsupervised learning.
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1.3 Critically analyse the role of gradient-based optimization in deep learning.
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1.4 Evaluate the impact of network depth on model performance.
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1.5 Discuss the theory of Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and introduce Diffusion Models.
2. Be able to compare, characterise and quantitatively evaluate various deep learning approaches.
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2.1 Describe and compare different architectures used in deep learning.
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2.2 Critique the performance of CNNs in image analysis tasks.
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2.3 Analyse the effectiveness of RNNs for sequential data processing.
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2.4 Critically evaluate the performance of generative models in various tasks, including GANs and Diffusion Models.
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2.5 Discuss transformer-based approaches to Large Language Models (LLMs).
3. Understand the limitations of deep learning.
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3.1 Identify common challenges faced in deep learning.
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3.2 Explain the concept of overfitting and strategies to mitigate it.
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3.3 Analyse the ethical considerations associated with deep learning.
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3.4 Critically evaluate the robustness and generalisation ability of deep learning models.
4. Be able to apply deep learning techniques to real-world problems in computer vision, speech, text analysis, and graph processing.
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4.1 Implement CNNs for image classification tasks.
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4.2 Apply RNNs to speech recognition and machine translation tasks.
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4.3 Develop generative models for synthetic data generation.
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4.4 Implement graph neural networks (GNNs) as a generalisation of CNN theory for graph-based data analysis.
Topics:
Introduction to Deep Learning
Course Coverage:
- Overview of Deep Learning
- Definition and Scope of Deep Learning
- Historical Context and Evolution of Neural Networks
- Biological Basis of Neural Networks: Non-linear activations and spiking neurons
- Importance of Deep Learning in Modern AI.
Learning Paradigms in AI
Course Coverage:
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Supervised Learning
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Basic Concepts and Examples
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Applications in Classification and Regression
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Unsupervised Learning
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Fundamental Techniques (Clustering, Dimensionality Reduction)
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Use Cases and Challenges
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Generalization and Overfitting
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Definitions and Differences
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Implications for Model Training
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Perceptrons and Neural Networks
Course Coverage:
- Perceptrons
- Single-Layer Perceptrons: Structure and Function
- Limitations and the XOR Problem
- Multi-Layer Perceptrons (MLP)
- Deep vs. Shallow Networks
- Advantages of Deep Architectures
Gradient-Based Optimization in Deep Learning
Course Coverage:
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Stochastic Gradient Descent (SGD)
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Concept and Mathematical Foundation
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Variants of SGD (Momentum, Nesterov)
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Backpropagation
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Derivation and Role in Neural Network Training
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Practical Implementation of Backpropagation
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Introduction to Generative Models
Course Coverage:
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Theory of Variational Autoencoders (VAE)
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Mathematical Principles of VAEs
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Applications of VAEs in Data Compression and Generation
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Diffusion Models
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Overview of Diffusion Models and Their Role in Generative Modeling
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Comparison of Deep Learning Architectures
Course Coverage:
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Perceptrons and Neural Networks:
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Single-Layer vs. Multi-Layer Perceptrons
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Non-Linear Activation Functions
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Convolutional Neural Networks (CNNs)
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Mathematical Foundations
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Convolution Operations
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Pooling Layers: Max and Average Pooling
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CNN Architectures
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Classic Architectures: LeNet, AlexNet, VGG
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Advanced Architectures: ResNet, Inception
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Recurrent Neural Networks (RNNs)
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Understanding RNNs
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Sequential Data Processing
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Backpropagation Through Time (BPTT)
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RNN Variants
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Long Short-Term Memory (LSTM)
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Gated Recurrent Units (GRU)
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Applications of RNNs
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Speech Recognition
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Machine Translation.
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Generative Models
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Introduction to Generative Data Augmentation Techniques
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Types: VAEs, GANs
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Mathematical Principles of Generative Models
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Generative Adversarial Networks (GANs)
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Structure and Functioning of GANs
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Challenges in Training GANs (Mode Collapse, Convergence Issues)
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Applications of Generative Models
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Image Generation and Enhancement
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Data Augmentation Techniques
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Incorporate Diffusion Models into Generative Models
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Performance Evaluation of Deep Learning Models
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Performance Metrics
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Accuracy and Loss Metrics
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Cross-Entropy, Mean Squared
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Error
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Evaluation Techniques
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Confusion Matrix, Precision, Recall, F1-Score
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ROC Curves and AUC
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Case Studies in Deep Learning
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Image Analysis
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Performance of CNNs in Image Classification
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Object Detection and Semantic Segmentation
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Sequential Data Processing
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RNNs in Time Series Forecasting
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LSTMs in Language Modeling
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Generative Networks
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VAEs vs. GANs in Image Generation
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Applications in Art and Content Creation
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- Introduction to Transformer Models
- Image Transformers in LLMs
- Attention Mechanisms and Their Role in Natural Language Processing
Challenges in Deep Learning
- Data Requirements
- Large Datasets
- The Need for Big Data in Training Deep Models
- Challenges in Data Collection and Labeling
- Imbalanced Data
- Techniques to Handle Imbalanced Datasets
- Synthetic Data Generation (SMOTE, ADASYN)
- Large Datasets
- Computational Costs
- Hardware Requirements
- Role of GPUs and TPUs in Deep Learning
- Impact of Computational Power on Model Training
- Optimization Techniques
- Techniques to Reduce Computational Load
- Distributed Training and Model Parallelism
- Hardware Requirements
- Interpretability and Transparency
- The Black Box Problem
- Lack of Transparency in Deep Models
- Importance of Model Interpretability
- Techniques for Interpretability
- Saliency Maps, Grad-CAM, LIME
- Explainable AI (XAI) Approaches
- The Black Box Problem
Overfitting and Generalization in Deep Learning
- Overfitting in Deep Models
- Causes of Overfitting
- High Model Complexity
- Insufficient Training Data
- Strategies to Mitigate Overfitting
- Regularization Techniques: L1, L2, Dropout
- Early Stopping and Cross-Validation
- Causes of Overfitting
- Generalization Challenges
- Transfer Learning
- Concepts and Benefits
- Pre-Trained Models and Fine-Tuning
- Adversarial Examples
- Impact on Model Robustness
- Techniques to Improve Robustness
- Transfer Learning
- Ethical Considerations in Deep Learning
- Bias in AI Models
- Sources and Implications of Bias
- Strategies to Mitigate Bias
- Privacy Concerns
- Data Privacy in AI Applications
- Techniques to Ensure Data Security
- Fairness and Accountability
- Ensuring Ethical AI Practices
- Case Studies in Ethical AI Implementation
- Bias in AI Models
Computer Vision with Deep Learning
- Introduction to Attention Mechanisms
- Convolutional Neural Networks (CNNs) Transfer Learning in CNNs
Speech and Language Processing with RNNs
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Applications in Real-Time Translation Systems
Generative Models for Synthetic Data Creation
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Diffusion Models for Generative Data Augmentation
Graph Neural Networks (GNNs)
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Introduce GNNs as a Generalization of CNN Theory
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Applications in Medicine and Biology
Indicative reading list
Core texts:
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Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
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Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS.
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Zhang, Y., et al. (2019). Graph Neural Networks: A Review of Methods and Applications. arXiv: 1812.08434
Additional reading:
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MIT Deep Learning for Self-Driving Cars: https://deeplearning.mit.edu/
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NVIDIA Deep Learning Institute: www.nvidia.com/dli
Entry requirements
To enroll The Master Award, the learner must possess:
- Graduated with a Bachelor’s degree from an accredited university or achieved a Level 6 Diploma according to the European Qualifications;
- For a degree from non-recognized universities; The learner should have followed Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) policy of MI Swiss and/or University Partners;
- Learners must be over 21 years old.
English requirements
If a learner is not from a predominantly English-speaking country, proof of English language proficiency must be provided.
- Common European Framework of Reference (CEFR) level B2 or equivalent;
- Or A minimum TOEFL score of 101 or IELTS 5.5; Reading and Writing must be at 5.5 or equivalent.
The UKeU reserves the highest decision-making authority regarding admissions and may accept or reject applicants following a thorough review of each applicant’s profile, ensuring that only those capable of benefiting from the course are admitted. Qualifications from diploma mills or fake universities/institutions will not be accepted by UKeU and/or Partner University.
After graduating with Master Award, learners receive all certified documents from the UKeU.
Certified Documents:
- e-Certificate from the UK eUni Worldwide (UKeU);
- Hard copy certificate from UK eUni Worldwide (UKeU) (Optional);
- Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from UKeU for credit and tuition fee transfer.
Because the course is accredited and recognized, learners can easily use their qualifications in the workplace and enjoy many opportunities for career advancement. In addition, if you wish to pursue a degree from UKeU and/or a Partner University, all credits and 50% paid tuition fees can be transferred.
The UKeU’ Master Award means:
UKeU Master Award is the award at the master level and is equivalent to:
- Level 7 certificate of Regulated Qualifications Framework (RQF) of UK
- Level 10 certificate of Scottish Credit and Qualifications Framework (SCQF)
- Level 7 certificate of Credit and Qualifications Framework (CQFW)
- Level 7 certificate of European Qualifications Framework (EQF)
- Level 9 certificates of the Australian Qualifications Framework (AQF)
- Level 7 certificate of ASEAN Qualifications Reference Framework (AQRF)
- Level 9 certificate of the African Continental Qualifications Framework (ACQF)
Learners can transfer all credits and 50% of their tuition fees when enrolling in UKeU and/or Partner University academic programs if they wish to pursue an academic degree.
Credits transfer:
Learners can transfer 20 credits from the Master Award course when participating in the Master program. Please see the credit transfer policy HERE.
Tuition fee transfer:
When enrolling in the Master program, graduates from the Master Award will receive a fee reduction equal to 50% of the tuition fees paid for the Master Award. Please refer to the tuition fee transfer policy HERE.
The UKeU Micro Degree course allows learners to transfer credits and 50% of their tuition fees toward full degree programs offered by UKeU and/or Partner University. UKeU reserves the right to limit admissions once enrollment exceeds the set quotas.
Apply Policy:
- To participate in the UKeU Micro Degree course, learners need to meet the entry criteria corresponding to each level. Please see the “Entry” tab for more details.
- UKeU will not accept applicants whose entry qualifications are from fake universities or institutions that are not accredited.
- For Master Award courses, if an entry bachelor is unavailable, learners must demonstrate a minimum of 5 years of work experience in the relevant field. Please note that a bachelor’s degree is required for the Master’s program at UKeU and Partner University so that you could study Master Award but could not move to the Master’s program of UKeU and/or Partner University.
- English is not a mandatory entry requirement for Micro Degree course, but candidates need to ensure that English is used in reading documents, listening to lectures, and doing assignments. Applicants should note that English is a mandatory requirement when switching to an academic program at UKeU and Partner University.
Apply Process:
- Step 1: To request a consultation for a course that best suits your needs, please email support@ukeu.uk. Our admissions department will contact you to guide you through the required documentation and the next steps in the application process.
- Step 2: Once your application documents are approved and the application fee is paid, UKeU will issue a Letter of Acceptance (LOA). You will then follow the provided instructions, including payment of the tuition fee.
- Step 3: After the tuition fee is paid, UKeU will issue a confirmation letter, provide your login details for the e-learning system, and send you all relevant documents.
- At this point, you have officially become a UKeU student. Welcome, and enjoy your learning journey!
The UKeU Micro Degree course is fully online, allowing you to study anytime and anywhere. You also have the option to attend live classes with UKeU. Final exams will be uploaded to the system and assessed by the UKeU academic board. Learners are required to submit assignments on time; failure to do so will require payment of a resit fee (with up to two attempts allowed). Continued non-compliance on a third occasion will result in being considered as having discontinued the course, and tuition fees will not be refunded.
Pricing Plans
Take advantage of one of our non-profit professional certified courses with favorable terms for your personal growing carreers.
- Live Class (Optional)
- Full online videos
- e-Books
- Self study contents
- Online tutor videos
- Assignment guide
- e-Certificate
- Hard copy certificate from UKeU and/or Partner Universities
- APEL.Q certified from UKeU for credit and tuition fee transfer
- Deliver hard copy certificate and all certified documents to your home
- Transfer full credits & 50% tuition fees to equivalent academic programs
- Opportunity to get scholarships when becoming Partner Universities' international students
UKeU MICRO DEGREE
Contact us
If you interested this micro credential course, please feel free to contact with us! Please note that this program is a not for profit and learning with full online model.