Machine Learning Course And Training
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
About this course
What you'll learn
01: Introduction  Introduction to the course  What is machine learning?  Supervised learning  introduction  Unsupervised learning  introduction
02: Regression Analysis and Gradient Descent  Linear Regression  Linear regression  implementation (cost function)  A deeper insight into the cost function  simplified cost function  Gradient descent algorithm  So no need to change alpha over time  Linear regression with gradient descent
03: Linear Algebra review  Matrices  overview  Vectors  overview  Matrix manipulation  Implementation/use  Matrix multiplication properties  Inverse and transpose operations
04: Linear Regression with Multiple Variables  Linear regression with multiple features  Gradient descent for multiple variables  Gradient Decent in practice: 1 Feature Scaling  Learning Rate a  Features and polynomial regression  Normal equation
05: Logistic Regression  Classification  Hypothesis representation  Decision boundary  Nonlinear decision boundaries  Cost function for logistic regression  Simplified cost function and gradient descent  Advanced optimization  Multiclass classification problems
06: Regularization  The problem of overfitting  Cost function optimization for regularization  Regularized linear regression  Regularization with the normal equation  Advanced optimization of regularized linear regression
07: Neural Networks  Representation  Neural networks  Overview and summary  Model representation 1  Model representation II  Neural network example  computing a complex, nonlinear function of the input  Multiclass classification
08: Neural Networks  Learning  Neural network cost functionx  Summary of what's about to go down  Backpropagation algorithm  Backpropagation intuition  Implementation notes  unrolling parameters (matrices)  Gradient checking  Random initialization  Putting it all together
09: Advice for applying machine learning techniques  Deciding what to try next  Evaluating a hypothesis  Model selection and training validation test sets  Diagnosis  bias vs. variance  Regularization and bias/variance  Learning curves
10: Machine Learning System Design  Machine learning systems design  Prioritizing what to work on  spam classification example  Error metrics for skewed analysis  Trading off precision and recall  Data for machine learning
11: Support Vector Machines  Support Vector Machine (SVM)  Optimization objective  Large margin intuition  Large margin classification mathematics (optional)  Kernels  1: Adapting SVM to nonlinear classifiers  Kernels II
12: Clustering  Unsupervised learning  introduction  Kmeans algorithm  K means optimization objective  How do we choose the number of clusters?
13: Dimensionality Reduction  Motivation 1: Data compression  Motivation 2: Visualization  Principle Component Analysis (PCA): Problem Formulation  PCA Algorithm  Reconstruction from Compressed Representation  Choosing the number of Principle Components  Advice for Applying PCA
14: Anomaly Detection  Anomaly detection  problem motivation  The Gaussian distribution (optional)  Anomaly detection algorithm  Developing and evaluating and anomaly detection system  Anomaly detection vs. supervised learning  Choosing features to use  Multivariate Gaussian distribution  Applying multivariate Gaussian distribution to anomaly detection
15: Recommender Systems  Recommender systems  introduction  Content based recommendation  Collaborative filtering  overview  Collaborative filtering Algorithm  Vectorization: Low rank matrix factorization  Implementation detail: Mean Normalization
16: Large Scale Machine Learning  Learning with large datasets  Stochastic Gradient Descent  Mini Batch Gradient Descent  Stochastic gradient descent convergence  Online learning  Map reduce and data parallelism
17: Application Example  Photo OCR  Problem description and pipeline  Sliding window image analysis
18: Course Summary
Course Details

Duration: 30 hours

hours effort: 5 hours per week

Price With GST: 23600/

Subject: Data Science

Level: Beginner
Prerequisites
Students are expected to have the following background: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably nontrivial computer program. Familiarity with the probability theory. Familiarity with linear algebra.