Machine Learning
Programme Overview
Duration
MQA Accredited
Brochure
Online Application
Enquiry
This course provides a deep dive into the fundamental concepts and practical techniques essential for understanding and implementing machine learning algorithms. Spanning fourteen weeks, the curriculum is structured to progressively build expertise, starting with mathematical foundations, and advancing through classical and contemporary machine learning methodologies
Lecterur Name: Raman Raguraman
Programme StructureProgramme Outcome
Programme Structure
Programme Duration:
Total Duration: 10 weeks
Learning Hours: 40 Hours
| Week | |
|---|---|
| 1 | Mathematical Basics 1 – Introduction to Machine Learning, Linear Algebra Introduction to Machine Learning: Overview of Machine Learning (ML),Types of ML: Supervised, Unsupervised, Reinforcement Learning,Key applications and examples of ML Linear Algebra: Vectors, matrices, and their operations,Matrix multiplication, determinants, and inverses Eigenvalues and eigenvectors,Singular Value Decomposition (SVD),Applications of linear algebra in ML |
| 2 | Mathematical Basics 2 – Probability Probability Theory: Basic probability concepts (events, sample spaces, conditional probability) Random variables, probability distributions (discrete and continuous) Expectation, variance, and standard deviation Common probability distributions: Bernoulli, Binomial, Poisson, Normal, Exponential Central Limit Theorem Applications in Machine Learning: Probabilistic models,Bayesian inference basics |
| 3 | Computational Basics – Numerical Computation and Optimization, Introduction to Machine Learning Packages Numerical Computation: Basics of numerical analysis, Floating-point arithmetic and errors,Numerical integration and differentiation, Optimization: Gradient Descent and its variants (Stochastic, Mini batch) , Convex optimization, Optimization challenges in ML (saddle points, local minima) Machine Learning Packages: Introduction to Python and Jupyter Notebooks, Overview of ML libraries: NumPy, SciPy, sci-kit-learn, TensorFlow, Keras, PyTorch Hands-on exercises with basic ML algorithms using these libraries |
| 4 | Week 4: Linear and Logistic Regression – Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications Linear Regression: Model representation, assumptions,Ordinary Least Squares (OLS) method Cost function and Gradient Descent for Linear Regression,Evaluation metrics: RMSE, R² Logistic Regression: Model representation, assumptions,Sigmoid function, cost function,Gradient Descent for Logistic Regression,Evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC Bias/Variance Tradeoff: Understanding bias and variance Strategies to balance bias and variance Regularization: L1 (Lasso) and L2 (Ridge) regularization,Regularization impact on model complexity Variants of Gradient Descent: Stochastic Gradient Descent (SGD),Mini-batch Gradient Descent |
| 5 | Neural Networks – Multilayer Perceptron, Backpropagation, Applications Multilayer Perceptron (MLP): Architecture and components Activation functions (ReLU, Sigmoid, Tanh) Forward propagation, Backpropagation, Derivation and intuition, Chain rule for gradients Implementation of backpropagation in MLPs,Applications: |
| 6 | Convolutional Neural Networks 1 – CNN Operations, CNN Architectures CNN Operations: Convolution operation, Pooling (Max, Average), Padding and Stride CNN Architectures: Layer types and their roles (Convolutional layers, Pooling layers, Fully Connected layers) Classic architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet Hands-on Implementation: Building simple CNN models using ML libraries |
| 7 | Convolutional Neural Networks 2 – Training, Transfer Learning, Applications Training CNNs: Techniques for improving training (Data Augmentation, Dropout, Batch Normalization) Overfitting and regularization in CNNs Transfer Learning: Concept and benefits Pre-trained models and fine-tuning Applications: Practical applications of CNNs in image classification, object detection, segmentation Sequence modeling applications (text generation, language translation, time series prediction) Application in clustering and density estimation Applications: Practical examples and use cases of clustering and density estimation techniques. |
| 8 | Recurrent Neural Networks (RNN), LSTM, GRU, Applications Recurrent Neural Networks (RNN): Architecture and functionality Challenges (vanishing/exploding gradients) Long Short-Term Memory (LSTM): LSTM cell architecture and components Advantages over standard RNNs Gated Recurrent Unit (GRU): GRU cell architecture and components Comparison with LSTM Applications: |
| 9 | Classical Techniques 1 – Bayesian Regression, Binary Trees, Random Forests, SVM, Naïve Bayes, Applications Bayesian Regression: Bayesian approach to linear regression Predictive distribution Decision Trees: Tree structure, splitting criteria. Pruning and overfitting Random Forests: Ensemble method, bagging Feature importance Support Vector Machines (SVM): Hyperplanes and margins Kernel trick Naïve Bayes: Assumptions, likelihoods Variants (Gaussian, Multinomial) |
| 10 | Classical Techniques 2 – k-Means, kNN, GMM, Expectation Maximization, Applications k-Means Clustering: Algorithm steps, choosing k Evaluation metrics (Inertia, Silhouette Score) k-Nearest Neighbors (kNN): Distance metrics (Euclidean, Manhattan) Weighting and voting schemes Gaussian Mixture Models (GMM): Mixture of Gaussians, responsibilities |
Programme Outcome
Upon successful completion of this 10-week intensive course, participants will be able to:
- Outcome 1: Master Fundamental Mathematical Concepts for Machine Learning:
- Develop a solid understanding of linear algebra and probability theory.
- Gain proficiency in numerical computation and optimization techniques.
- Learn to implement mathematical principles using popular machine-learning packages.
- Outcome 2: Design and Implement Core Machine Learning Models
- Acquire skills to design, train, and evaluate linear and logistic regression models, and neural networks (MLP, CNN, RNN, LSTM, GRU), and understand their applications.
- Understand and apply regularization techniques, bias/variance tradeoffs, and gradient descent variants.
- Utilize transfer learning for advanced model performance.
- Outcome 3: Apply Classical and Modern Machine Learning Techniques:
- Explore and apply classical machine learning techniques like Bayesian regression, binary trees, random forests, SVM, Naïve Bayes, k-Means, kNN, GMM, and Expectation Maximization.
- Implement these techniques in practical scenarios to solve real-world problems.
- Compare and contrast classical techniques with modern neural network-based approaches for various applications
