About the Course Python For Machine Learning - CST 283 - KTU Minor



CST283 :PYTHON FOR MACHINE LEARNING
CATEGORY:MINOR
L T P :3 1 0
CREDIT: 4
YEAR OF INTRODUCTION:2019 

Preamble: This is a programming course for awarding B. Tech. Minor in Computer Science and  Engineering with specialization in Machine Learning. The objective of the course is to provide learners an insight into Python programming, and develop programming skills to manage the development of software systems. It covers programming environment, important instructions,data representations, intermediate level features, Object Oriented Programming and file data processing of Python. This course lays the foundation to develop web applications, Machine Learning, and Artificial Intelligence-based applications and tools, Data Science and Data Visualization applications.

Prerequisite: Nil

Mark Distribution
Total Marks     CIE Marks     ESE Marks     ESE Duration
150                     50                 100                     3

Continuous Internal Evaluation Pattern:
Attendance : 10 marks
Continuous Assessment Test : 25 marks
Continuous Assessment Assignment : 15 marks 

Internal Examination Pattern

Each of the two internal examinations has to be conducted out of 50 marks. The first series test shall be preferably conducted after completing the first half of the syllabus and the second series test shall be preferably conducted after completing the remaining part of the syllabus. There will be two parts: Part A and Part B. Part A contains 5 questions (preferably, 2 questions each from the completed modules and 1 question from the partly completed module), having 3 marks for each question adding up to 15 marks for part A. Students should answer all questions from Part A. Part B contains 7 questions (preferably, 3 questions each from the completed modules and 1 question from the partly completed module), each with 7 marks. Out of the 7 questions, a student should answer any 5.


End Semester Examination Pattern:

There will be two parts; Part A and Part B. Part A contains 10 questions with 2 questions from each module, having 3 marks for each question. Students should answer all questions. Part B contains 2 questions from each module of which a student should answer any one. Each question can have a maximum of 2 sub-divisions and carries 14 marks.

Course Outcomes: After the completion of the course the student will be able to

CO1:Write, test and debug Python programs (Cognitive Knowledge level: Apply)
CO2: Illustrate uses of conditional (if, if-else, if-elif-else and switch-case) and iterative
(while and for) statements in Python programs (Cognitive Knowledge level: Apply)
CO3: Develop programs by utilizing the modules Lists, Tuples, Sets and Dictionaries in Python (Cognitive Knowledge level: Apply)
CO4: Implement Object Oriented programs with exception handling (Cognitive Knowledge level: Apply)
CO5: Write programs in Python to process data stored in files by utilizing the modules Numpy, Matplotlib, and Pandas (Cognitive Knowledge level: Apply)

Sample CO Level Assessment Questions
Course Outcome1(CO1): What is type conversion? How is it done in Python?
Course Outcome 2(CO2): Write a Python program which takes a positive integer n as input and finds the sum of cubes all positive even numbers less than or equal to the number.
Course Outcome 3(CO3): Given is a list of of words, wordlist, and a string, name. Write a Python function which takes wordlist and name as input and returns a tuple. The first element of the output tuple is the number of words in the wordlist which have name as a substring in it. The second element of the tuple is a list showing the index at which the name occurs in each of the words of the wordlist and a 0 if it doesn’t occur.
Course Outcome 4(CO4): Write a Python program to implement the addition, subtraction, and multiplication of complex numbers using classes. Use constructors to create objects. The input to the program consist of real and imaginary parts of the complex numbers.
Course Outcome 5(CO5): Given a file “auto.csv” of automobile data with the fields index, company, body-style, wheel-base, length, engine-type, num-of-cylinders, horsepower, average-mileage, and price, write python code to
1) Clean and Update the CSV file
2) Print total cars of all companies
3) Find the average mileage of all companies
4) Find the highest priced car of all companies.

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