This course was taught earlier under CE623 and shares/shared significant part of it's syllabus with 2IT422 - Data Engineering, 2CE423 - Data Warehousing and Mining, CE633 & 2CSDE71 - Data Mining. With this repository I aim to create a guide to acing 2CS501 and its analogs
| Course Code | 2CS501 |
|---|---|
| Course Name | Machine Learning |
| Unit | Syllabus | Teaching Hours |
|---|---|---|
| I | Introduction: Motivation and Applications, importance of Data Visualization, Basics of Supervised and Unsupervised Learning | 3 |
| II | Regression Techniques: Basic concepts and applications of Regression, Simple Linear Regression – Gradient Descent and Normal Equation Method, Multiple Linear Regression, Non-Linear Regression, Linear Regression with Regularization, Hyper-parameters tuning, Loss Functions, Evaluation Measures for Regression Techniques | 14 |
| III | Classification Techniques: Naïve Bayes Classification, Fitting Multivariate Bernoulli Distribution, Gaussian Distribution and Multinomial Distribution, K-Nearest Neighbours, Decision trees. Support Vector Machines: Hard Margin and Soft Margin, Kernels and Kernel Trick, Evaluation Measures for Classification Techniques |
10 |
| IV | Artificial Neural Networks: Biological Neurons and Biological Neural Networks, Perceptron Learning, Activation Functions, Multilayer Perceptrons, Back-propagation Neural Networks, Competitive Neural Networks | 9 |
| V | Clustering: Hierarchical Agglomerative Clustering, k-means Algorithm, Self-Organizing Maps | 4 |
| VI | Advanced Concepts: Basics of Semi-Supervised and Reinforcement Learning, Linear Discriminant Analysis, Introduction to Deep Learning | 5 |