Department Events
Advanced Machine Learning
This course will enable the students
1. To understand the fundamentals of data science, AI and ML.
2. To apply data preprocessing, feature engineering, and optimization techniques to solve complex problems.
3. To analyze machine learning models, evaluate their performance, and apply them to real-world applications.
4. To explore advanced machine learning techniques, including deep learning, generative models, and reinforcement learning.
| Topics | Teaching Hours |
Unit I | Introduction to Data Science AI and ML Data-Science: What is Data Science? – The core problems and solutions. Extracting Intelligence from Data– formulating problems, The Data Pipeline Types of Data in various practical Data Science scenarios. Data Wrangling, Cleaning and Preparation. Data Science Lifecycle. AI vs Machine Learning vs Deep Learning- Overview of the relationships between AI, ML, and DL .Real-world applications in various industries (healthcare, finance, robotics) Ethical Considerations in AI and ML- Sources of bias in data and models, Case studies: facial recognition bias, hiring algorithms. Techniques for detecting and mitigating bias.
| 12 |
Unit II | Data Processing and Feature Engineering Data Preprocessing:Handling Missing Data, Outliers, and Data Cleaning - Normalization, Standardization, Encoding Categorical Data Feature Engineering:Feature Extraction and Selection - Dimensionality Reduction: PCA, t-SNE, LDA, Autoencoders.
| 12 |
Unit III | Machine Learning Algorithms and Applications- Supervised Learning:Linear Regression, Logistic Regression - Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM, CatBoost) - Support Vector Machines (SVM) Unsupervised Learning:Clustering: K-Means, DBSCAN, Hierarchical Clustering Neural Networks and Deep Learning: Perceptron, Multi-Layer Perceptron (MLP) - Convolutional Neural Networks (CNNs) for Image Processing - Recurrent Neural Networks (RNNs), LSTM.
| 12 |
Unit IV | Model Evaluation and Interpretability: Bootstrapping and Cross Validation, k-fold cross validation, Two Class Evaluation Measures, Confusion Matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, F1-score, Precision Recall curve, Break Even Point, ROC Curve, Area Under Curve(AUC), Minimum Description Length and Exploratory Analysis. Model Interpretability Techniques: SHAP, LIME
| 12 |
Unit VI | Advanced Machine Learning and Deep Learning algorithms: Reinforcement Learning:Markov Decision Processes (MDP) - Q-Learning, Deep Q-Networks (DQN) - Policy Gradient Methods Generative Models:Variational Autoencoders (VAE) - Generative Adversarial Networks (GANs).
| 12 |
References:
- Karl Beecher,”Computational Thinking: A beginner's guide to problem-solving and
- programming”,BCS Learning & Development Limited,2017
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (1st ed.). Springer.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (1st ed.). MIT Press.
- Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.
- Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (1st ed.). MIT Press.
