What is Machine Learning?
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Syllabus
Sr. No | Title | Duration (Hrs.) |
1. | Introduction, what is Machine Learning? Types and Applications of ML, AI vs ML vs DL | 1 |
2. | An Overview on Math for ML, AI and DL
Linear Algebra: Scalars, Vectors and Matrices, Variance (Var), Covariance (Cov), Covariance Matrix, Eigenvector and Eigen value Probability: Joint Probability Distribution, Conditional Probability Distribution, The Rules of Probability, Bayes’ Rule, Independence, Continuous Random Variables, Bernoulli, Multinomial, Normal (Gaussian) Distribution Calculus: Geometric definition, Taking the derivative, Chain rule Gradients, Partial derivatives |
4 |
3. | Types of Machine Learning and Demonstration.
1. Supervise, 2. Unsupervise 3. Reinforcement 4. Deep Learning |
1 |
4. | NLP: WordNet, Sentiment Analysis and Opinions on the Web, Phonology, POS Tagging. | 2 |
5. | Supervise: Regression and Classificaton, Linear Regression, Polynominal Regression, KNN, Naïve Bayes,
Logistic Regrassion, Cost Function, Optimizing Algoritham(Gradient decent) |
6 |
6. | Unsupervised: Clustering and Association analysis, K-Means, PCA, Apriori algorithm, Elcat algorithm | 6 |
7. | Reinforcement: HMM, Q-Learning, DQL | 4 |
8. | Deep Learning: Nural Network, Types os NN, The Perceptron, The Backpropagation Algorithm, Activation Function, Cost function | 4 |
9. | Optimization: Optimization algorithm, Gradient Decent, BFGS, Genatic Algoritham, | 4 |
10. | Data Rescalling and Encodding: Label encoding, Label binarization, Standardize data, MinMaxScalr, MaxAbsScaler, RobustScaler, PowerTransformer, QuantileTransformer (Gaussian output), QuantileTransformer (uniform output) | 3 |
Course Outcome
On completion of this course both undergraduate and graduate students will be able to:
- recognize current real-world problems that can benefit from emerging machine learning techniques.
- Classify the characteristics of datasets.
- understand machine learning techniques and computing environment that are suitable for the applications under consideration.
- implement various ways of selecting suitable model parameters for different machine learning techniques.
- integrate machine learning libraries, mathematical and statistical tools with modern technologies.
- implement solutions using the machine learning techniques and the programming framework to obtain acceptable decisions for the real-world problems that they used for the experiment.
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