Course

Module 1: Introduction to Computational Thinking – Abstraction, Decomposition, Pattern Recognition – Algorithm Design: Sequence, Selection, Repetition – Logical Reasoning and Flowcharts-Problem formulation and structured thinking Case study: Simple real-life problem modeling using algorithm. Problem Solving with Algorithms – Searching and Sorting – Applied Computational Thinking Problems.

Module 2: Python basics: Syntax, Data Types, Variables, I/O – Control Flow: Conditionals and Loops -Data Structures: Lists, Tuples, Dictionaries, Sets – Functions and Recursion – File Handling and Exception Handling – Debugging and Code Tracing. Python Libraries, Text Processing, Data Processing and Analysis, Chatbot etc.

Module 3: Data Science: Data Pre-processing: Data cleaning, Data reduction, Data transformation, Data discretization. Visualization and Graphing: Visualizing Categorical and Numerical Distributions, Dimension Reduction: Curse of Dimensionality, Practical Considerations, Correlation Analysis, Principal Components Analysis, Dimension Reduction Using Regression, Classification, Linear Discriminant Analysis.

Module 4: Introduction to Machine Learning: Supervised learning – Regression: Linear regression, logistic regression – Classification: K-Nearest Neighbor, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, Perceptron, Error analysis. Unsupervised learning – Clustering: K-means, Hierarchical, Spectral, subspace clustering, Introduction to Neural Networks, Reinforcement learning and generative learning.

Suggested Lab Sessions:

·         Implementation of Flowgorithm / Equivalent for Algorithms.

·         Overview of Python Programming / Equivalent.

·         Implementation of contents of the course using Python / Equivalent.

CO1: Apply pre-processing techniques to prepare the data for machine learning applications CO2: Implement supervised machine learning algorithms for different datasets

CO3: Implement unsupervised machine learning algorithms for different datasets

CO4: Analyze the error to improve the performance of the machine learning models

Textbooks / References:

1.      Andrew Ng, Machine learning yearning, URL: http://www. mlyearning. org/(96) 139 (2017).

2.      Kevin P. Murphey. Machine Learning, a probabilistic perspective. The MIT Press Cambridge, Massachusetts, 2012.

3.      Christopher M Bishop. Pattern Recognition and Machine Learning. Springer 2010

4.      Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. Wiley, Second Edition;2007

5.      Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.

6.      John Hopcroft and Ravi Kannan, “Foundations of Data Science”, ebook, Publisher, 2013.